research proposal on computational chemistry

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35911 Overview of the Research Proposal

• Published: August 2008
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In this module, we focus on writing a research proposal, a document written to request financial support for an ongoing or newly conceived research project. Like the journal article (module 1), the proposal is one of the most important and most utilized writing genres in chemistry. Chemists employed in a wide range of disciplines including teaching (high school through university), research and technology, the health professions, and industry all face the challenge of writing proposals to support and sustain their scholarly activities. Before we begin, we remind you that there are many different ways to write a successful proposal”far too many to include in this textbook. Our goal is not to illustrate all the various approaches, but rather to focus on a few basic writing skills that are common to many successful proposals. These basics will get you started, and with practice, you can adapt them to suit your individual needs. After reading this chapter, you should be able to do the following: ◾ Describe different types of funding and funding agencies ◾ Explain the purpose of a Request for Proposals (RFP) ◾ Understand the importance of addressing need, intellectual merit, and broader impacts in a research proposal ◾ Identify the major sections of a research proposal ◾ Identify the main sections of the Project Description Toward the end of the chapter, as part of the Writing on Your Own task, you will identify a topic for the research proposal that you will write as you work through this module. Consistent with the read-analyze-write approach to writing used throughout this textbook, this chapter begins with an excerpt from a research proposal for you to read and analyze. Excerpt 11A is taken from a proposal that competed successfully for a graduate fellowship offered by the Division of Analytical Chemistry of the American Chemical Society (ACS). As is true for nearly all successful proposals, the principal investigator (PI) wrote this proposal in response to a set of instructions. We have included the instructions with the excerpt so that you can see for yourself how closely she followed the proposal guidelines.

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Operando Photoelectrochemical-X-Ray-Spectroscopies-Led Rational Discovery of New Pyrochlore Oxides for Water Electrolysis (DIA24/EE/MPEE/TIWARI)

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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

To date, computational approaches have been recognized as a key component in drug design and discovery workflows. Developed to help researchers save time and reduce costs, several computational tools have been developed and implemented in the last twenty years. At present, they are routinely used to identify a therapeutic target, understand ligand–protein and protein–protein interactions, and identify orthosteric and allosteric binding sites, but their primary use remains the identification of hits through ligand-based and structure-based virtual screening and the optimization of lead compounds, followed by the estimation of the binding free energy. The repurposing of an old drug for the treatment of new diseases, helped by in silico tools, has also gained a prominent role in virtual screening campaigns.

Moreover, the availability and the decreasing cost of hardware and software, together with the development of several web servers on which to upload and download computational data, have contributed to the success of computer-assisted drug design. These improved, accurate, and reliable methods should help to add new and more potent molecules to the paraphernalia of approved drugs. Nevertheless, the ease of access of computational tools in drug design (software, databases, libraries, and web servers) should not encourage users with little or almost no knowledge of the underlying physical basis of the methods used, who could compromise the interpretation of the results. The figure of the computational (medicinal) chemist should be recognized and included in all research groups. These considerations led us to promote a volume collecting some original contributions regarding all aspects of the computational approaches, such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, and reverse modeling. We also include ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR. Drug design and the development process strive to predict the metabolic fate of a drug candidate to establish a relationship between the pharmacodynamics and pharmacokinetics and highlight the potential toxicity of the drug candidate. Even though the use of computational approaches is often combined, we tried to identify which of these play a central role in each manuscript.

In this Special Issue, the use of molecular dynamics simulations, both unbiased and biased, cover a major part of the contributions. Non-covalent inhibition of the immunoproteasome was investigated in-depth through MD-binding and binding pose metadynamics [ 1 ]. MD simulations provided insight into the structural features of hTSPO (Translocator Protein) and the previously unknown interplay between PK11195, a molecule routinely used in positron emission tomography (PET) for the imaging of neuroinflammatory sites, and cholesterol [ 2 ]. The interaction of certain endogen substrates, drug substrates, and inhibitors with wild-type MRP4 (WT-MRP4) and its variants G187W and Y556C were studied to determine differences in the intermolecular interactions and affinity related to SNPs using several approaches, but particularly all-atom, coarse-grained, and umbrella sampling molecular dynamics simulations (AA-MDS and CG-MDS, respectively) [ 3 ]. Natural sodium–glucose co-transporter 2 (SGLT2) inhibitors were selected to explore their potential against an emerging uropathogenic bacterial therapeutic target, i.e., FimH, which plays a critical role in the colonization of uropathogenic bacteria on the urinary tract surface, and molecular dynamics simulations were carried out to study the potential interactions [ 4 ]. Doxorubicin encapsulation in carbon nanotubes with haeckelite or Stone–Wales defects as drug carriers were investigated using a molecular dynamics approach [ 5 ]. The combined use of different approaches has been reported in a series of papers associated with the virtual screening of libraries. Almeelebia and co. screened 224,205 natural compounds from the ZINC database against the catalytic site of the Mtb proteasome [ 6 ]. Pharmacophore-based virtual screening and molecular docking were carried out to identify potential Src inhibitors starting from a total of 891 molecules. Finally, MD simulations identified two molecules as potential lead compounds against Src kinase [ 7 ]. An in silico study identified a methotrexate analog as a potential inhibitor of drug-resistant human dihydrofolate reductase for cancer therapeutics [ 8 ]. A structure-based method for high-throughput virtual screening aimed to identify new dual-target hit molecules for acetylcholinesterase, and the α7 nicotinic acetylcholine receptor was reported and confirmed in vitro [ 9 ]. A new complementary computational analysis called “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete in terms of possible binding sites on the antigen [ 10 ]. Interesting drug repurposing strategies have been reported. Hudson and Samudrala presented a computational analysis of a novel drug opportunities (CANDO) platform for shotgun multitarget repurposing. It implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures [ 11 ]. Qi and co. data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarities between gene expression signature (GES) profiles from drugs and their indicated diseases for GES-guided drug-repositioning approaches [ 12 ]. In late 2019, the SARS-CoV-2 pandemic focused the attention of many researchers intending to find not only vaccines but also new antiviral drugs. These reasons boosted the use of computational approaches to explore large libraries of natural compounds, already approved drugs, and in-house and commercial compounds [ 13 , 14 ]. In this issue, Baig and co. studied the efficacy of the Mpro inhibitor PF-00835231 against Mpro and its reported mutants in clinical trials. Several in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit Mpro [ 15 ]. Li and co. computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds to be used against SARS-CoV-2 [ 16 ]. Pedretti and co., exploiting the availability of resolved structures, designed a structure-based computational approach. The innovative idea of their study was to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns [ 17 ]. In the context of antiviral drugs, Regad and co. investigated the emergence of HIV-2 resistance. They proposed a structural analysis of 31 drug-resistant mutants of HIV-2 protease (PR2), an important target against HIV-2 infection [ 18 ]. A wide series of contributions regarding the use of QSAR, machine learning, and deep learning has reported interesting outcomes. A multiple-molecule drug design based on systems biology approaches and a deep neural network to mitigate human skin aging was developed by Yeh and co. With the proposed systems medicine design procedure, they not only shed light on the skin-aging molecular progression mechanisms, but they also suggested two multiple-molecule drugs to mitigate human skin aging [ 19 ]. The construction of quantitative structure–activity relationship (QSAR) models was used to predict the biological activities of the compounds obtained with virtual screening and identify new selective chemical entities for the COX-2 enzyme [ 20 ]. The three-dimensional QSAR model, employing a common-features pharmacophore as an alignment rule, was built on 20 highly active/selective HDAC1 inhibitors. The predictive power of the 3D QSAR model represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity [ 21 ]. Different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints were evaluated to develop quantitative structure–activity relationship (QSAR) models, which are important for hERG potassium channel blocker prediction [ 22 ].

Throughout this Special Issue, all the recent aspects of the computational approaches applied to several research fields are reported. We express our deep gratitude to all the contributors to this Special Issue for their commitment, hard work, and outstanding papers. We also thank all the reviewers involved in the manuscript revisions for their unpaid contributions to improve any aspects of the submitted works. Last but not least, we deeply thank Mrs. Jessie Zhang for her assistance during the period in which we served as guest editors.

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The authors declare no conflict of interest.

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Promoting the Development of Computational Chemistry Research: Motivations, Challenges, Options and Perspectives

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Computational chemistry is a fast growing area of modern chemistry, capable of interfacing with the other research areas in chemistry and with other sciences involving consideration of substances and materials, and enjoying increasing industrial relevance. Its presence in Sub-Sahara African tertiary institutions is still scarce, mostly because of scarcity of experts. This chapter analyses the current situation, discusses the importance of developing it and the relevance of such development for research and education, outlines its relevance for sustainable development, offers reflections for possible development pathways and a feasibility assessment based on the concrete experience of its recent development, ex novo , in an underprivileged university in South Africa.

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Mammino, L. (2013). Promoting the Development of Computational Chemistry Research: Motivations, Challenges, Options and Perspectives. In: Gurib-Fakim, A., Eloff, J. (eds) Chemistry for Sustainable Development in Africa. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29642-0_6

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MSI is proud to spotlight some of our researchers in a new series of articles that are aimed both at highlighting their research and illustrating how MSI facilitates it. Christopher Cramer’s group uses supercomputing resources to push the limits of present-day computational chemistry tools in order to examine large systems that are of relevance to one or more areas in chemistry. The group develops, codes, and applies novel molecular and quantum mechanical methodologies to model chemical structures, properties, and reactivities. Recently Professor Cramer sat down with an MSI staffer to talk about two Department of Energy grants that will fund his work, what research they will be funding, and what role MSI has played in his experience as a U of M researcher.

MSI: What research are the DOE grants funding?

Cramer: The two DOE grants will both start in September. One is a multi-institutional grant. It will provide a total of $8.1M in funding and it’s called the Nanoporous Materials Genome Center with Chemistry colleague Professor Laura Gagliardi as Director. It is designed to make predictions about and explain the properties of things called metal-organic frameworks (MOFs), which are a type of what are known as nanoporous materials. The other grant will be funded under DOE’s SciDAC program.  SciDAC stands for “Scientific Discovery through Advanced Computing.” The related project involves the collaboration of the University of Minnesota and the Pacific Northwest National Laboratory and will focus on excited-state processes of molecules and excited-state dynamics. In layman’s terms, the focus will be on how to take solar energy, which can be captured by molecules that absorb solar photons to attain electronically excited states, and transfer and use that energy effectively, in, say, a chemical reaction that generates a so-called solar fuel.

MSI: Can you describe the kinds of things you’re doing on Itasca?

Cramer: There are two separate projects, so let me talk about each of them separately, because they’re a little bit different.

The Nanoporous Materials Genome Center project has a database aspect to it. While we want to predict novel properties, at the same time we’re going to have to train our predictions on what’s known about existing properties. So, one goal of the project will be to create a database that, essentially, would allow one to look up everything that’s been done, and we’ll then be using that data in order to validate the models that we make, thus predicting things that aren’t yet known. But, will that involve terabytes of data? Probably not. There are only so many properties we would store, and there are only so many known systems. So it’s not quite a data-mining operation. In the very long run, though, we’d like to have enough confidence in the predictive models that the database can then be extended by blue-sky predictions. That is, for any combination of metal and organic frameworks, what structures and properties can be expected?

The SciDAC, which, again, deals with excited state chemistry and developing new models and putting them into code, does not really pose a data storage challenge. The computational demands are more associated with speed and memory. With quantum chemistry, the issue is generally the need for large memory and fast processor speeds rather than terabytes of storage. So, we’re going to design some software as well as new algorithms that will allow us to treat these excited states and do that in combination with a code developed at the PNL called NWChem, which, I believe, is already running on MSI machines. NWChem is a massively parallel quantum chemistry code that the PNL makes available under free license all around the world.

MSI : Is there any other software that MSI offers that you will utilize?

Cramer :  Yes, we’re heavy users of all sorts of quantum chemistry codes. Gaussian09 is probably the one my group uses the most, and some of the other codes installed at MSI that do quantum chemistry are MOLCAS, Turbomole, ORCA, ADF (Amsterdam Density Function) – those are probably the workhorses that get the most use in my group. They all sort of do the same thing, but each one of them has something that it does better than the others. That’s why we would – for certain problems, we would use one over the others. We will also write our own code, typically modules that interface with larger production codes where we have access to the source.

MSI: Let’s change directions a little bit: Is any of your group’s research being done by graduate students for their dissertations?

Cramer : Oh, absolutely. My group usually has around five graduate students at any given time; that’s sort of my historical average. I also have post-doctoral researchers who work with me, and quite a number of undergrads. I think I’ve probably advised 60 undergraduates in my time at Minnesota, and they’re all doing computational work at MSI.

And then, actually, although this is a research story, I guess I would mention that I teach a computational chemistry course every year in the department. Well, it’s not always I, but, anyway, I have taught it a lot. The students in that course get MSI accounts and they do practical labs, if you will, as part of the course, and that’s both seniors and first-year graduate students, who are the people who tend to take the course.

I have also taught a freshman seminar where – you know, freshman seminars, they’re pretty introductory ­– we read some books, and one of the books is called The Billion-Dollar Molecule , which is an interesting book written by the journalist Barry Werth. It’s about a start-up pharmaceutical company in Boston. It’s a very readable book, not a science book, but it does discuss the science of this molecule they’re going after. They’ve got an x-ray structure of the molecule, and they’re trying to figure out how to get the drug to bind to it. When we get to this point in the freshman seminar, we go on a little field trip to the MSI visualization lab because we can view the crystal structure there. The students get to see the protein rotating in 3D space, and really, the freshman love it. They say, “Wow! This is what the University is all about.”

MSI’s mission is to work with researchers like Christopher Cramer and his group to help promote the use of high performance computation in cutting-edge research while also promoting the education of postdoctoral, graduate, and undergraduate students here at the University of Minnesota. You can learn more about Professor Cramer and the Department of Chemistry’s Chemical Theory Center at their website. Another article about the DOE grants can be found on the College of Science and Engineering website .

Image: The time evolution of an electronic excited state of the dye alizarin attached to a model of solid titania. With time, an electron is shown to move from the molecule (above) into the titania cluster (below), a process known as "charge injection" and important for the capture of solar energy. (Graphic courtesy of K. Lopata and N. Govind, Cramer group collaborators at Pacific Northwest National Laboratory.)

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• Published: 28 September 2020

Computation sparks chemical discovery

Nature Communications volume  11 , Article number:  4811 ( 2020 ) Cite this article

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Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field.

Theoretical and computational modelling is ubiquitous in materials research. Modelling can significantly help to bridge the results of fundamental materials research to actual materials production by significantly reducing timescales. The computational chemistry approaches developed over the years have been an invaluable tool to provide deep insight into chemical processes beyond what can be directly measured experimentally. A new Collection [ https://www.nature.com/collections/ncomms-compchem ] showcases recent progress in developing these computational frameworks.

For many years, density functional theory (DFT) was considered the method of choice to study the electronic structure of molecules, materials and condensed systems, enabling an optimal trade-off between accuracy and computational cost. This balance could be achieved by including the complex many-body electron–electron interactions within a functional of the density, i.e. the exchange and correlation functional. During the 1980s and 1990s, thei key to the huge advances achieved by molecular simulations was to develop more and more accurate quantum-mechanical approximations in order to climb the so-called Jacob’s ladder, with each rung representing increasing levels of complexity and decreasing levels of approximation to the exact exchange and correlation functional. This led to the so-called chemical modelling revolution, as highlighted by Tkatchenko in his Comment entitled Machine learning for chemical discovery 1 .

Considering how the world has changed with the increasing availability of curated datasets containing reliable quantum-mechanical properties of molecules and materials, and how our ability to collect big data has greatly surpassed our capability to analyze it, a completely different strategy is to think about how seemingly unrelated data and properties may impact each other, studying the hidden interconnections between them. In this vein, an alternative approach to advance the predictive capability of computational approaches is to replace the physically motivated path by a data-driven search. This has given rise to big-data-driven science, which applies machine learning (ML) techniques to molecular and materials science. While ML approaches have been in use for decades for identifying correlations from big amounts of data, only recently has the computational community started to invest tremendously in programme infrastructures based on the synergetic collaboration between materials scientists, who have experimental and theoretical expertise, and computer scientists to develop ML methods aimed at discovering new molecules and materials. Under development are ML methodologies that combine electronic structure calculations and statistical analysis tools, which when fed with increasingly available molecular big data, can serve as alternatives to standard methods to explore the vast chemical space. In these ongoing efforts, the computational community currently faces theoretical and technical challenges.

Computational studies of chemical processes taking place over extended size and time scales must balance computational cost and accuracy: electronic structure methods are very accurate but computationally expensive, while atomistic models such as force fields—although computationally affordable—lack transferability to new systems.

In Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning , Smith et al. 2 discuss that an ideal solution to achieve the best of both approaches lies in developing a general purpose neural network potential that approaches CCSD(T) accuracy (coupled cluster considering single, double, and perturbative triple excitations), the gold standard in quantum chemistry, yet exhibits transferability over a broad chemical space. Most importantly for practical calculations, the resulting potential is an attractive alternative to DFT approaches and standard force fields: it is broadly applicable for conformational searches, molecular dynamics, and the calculation of reaction energies and is billions of times faster than CCSD(T) calculations.

Within traditional DFT modelling, seeking to increase the non-locality of the exchange and correlation functional in the effort to achieve more accurate approximations comes at a steep increase in computational cost, making related computational efforts impractical. A different approach in this area is to develop specialized ML functionals, whose overall accuracy does not significantly degrade when used outside their training scope.

Dick and Fernandez-Serra in Machine learning accurate exchange and correlation functionals of the electronic density tackle this problem by introducing a fully machine-learned functional that depends explicitly on the electronic density and implicitly on the atomic positions 3 . It approaches the accuracy of high level quantum chemistry methods at an affordable computational cost. Although these functionals were created for a specific dataset and hence are not universal, they exhibit promising transferability from the gas to condensed phase and from small to larger molecules within the same type of chemical bonding.

One common feature of machine learning approaches used in molecular simulations is that since the electronic properties are learned from quantum chemistry data, each individual model is typically limited to exploring these specific properties. Since all the physical and chemical features of a hypothetical compound can be derived by its ground-state electronic wavefunction, one way to circumvent this problem is to establish a direct link between ML and quantum chemistry with a ML model that predicts the ground-state wavefunction, as discussed by Schütt et al. in Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions 4 . The deep learning approach introduced by these authors provides full access to the electronic properties needed for practical calculations of reactive chemistry, such as charge populations, bond orders, and dipole and quadrupole moments, at a force-field-like efficiency. Moreover, the approach may enable property-driven chemical structure exploration, suggesting promise towards inverse-chemical design.

Although acknowledging the rapid evolution of computational techniques is exciting, this is not to suggest that traditional deep quantum chemistry expertise is obsolete: on the contrary, standard high-level theoretical approaches are still indispensable for solving fundamental problems in computational chemistry. A nice example is shown by Liu et al. 5 in The electronic structure of benzene from a tiling of the correlated 126-dimensional wavefunction. Using high-level correlated wavefunction theory, the authors revisit the electronic structure of benzene, which has been a test bed for competing theories throughout the years. In alternative to the traditional description of the electronic structure in terms of molecular orbital (MO) theory, the authors rely on a method to identify and visualise wavefunction tiles, known as dynamic Voronoi Metropolis sampling. The use of such high-level theory enables them to reveal the fundamental effect of electron correlation in benzene and show its manifestation in the preference for staggered Kekulé structures, whereas the interpretation of electronic structure in terms of MO theory ignores that the wavefunction is anti-symmetric upon interchange of like-spins.

ML algorithms and natural language processing approaches also offer new possibilities in optimizing and automating reaction procedures. On-demand synthesis of small drugs is of key interest in this area, where both the forward synthesis (given a set of reactants, predict the products) and the retrosynthesis (given a target, predict reactant and reagents) can strongly benefit from recent modelling advances. Reaction predictions are usually considered a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. The ultimate goal is to implement human-refined chemical recipe files to feed a robotic platform, which then execute the actual synthesis in an automated manner. A challenge here revolves around the need to extract chemical instructions from patents and the scientific literature, where they are reported in prose, and convert them to a machine-readable format. In Automated extraction of chemical synthesis actions from experimental procedures , Vaucher et al. 6 make a first important step towards implementing the automated execution of arbitrary reactions with robotic systems by developing a deep-learning model that performs the conversion of chemical instructions for organic synthesis reactions.

Although data-driven computational approaches clearly hold promise towards speeding up the discovery of new molecules and materials, at the moment current applications are only at the beginning of the exploration phase. The reliability of any ML approach depends on the availability of extensive datasets for model training, the bottleneck in cases where data is not abundant or difficult to generate. Along with the need for extensive curated data sets of microscopic and macroscopic molecular properties, future work should target the development of more transferable models with universal approximations that can treat local chemical bonding and non-local interactions on the same foot.

As an ultimate goal, the hope is to develop ML approaches that can not only provide predictive models but also interpretable models to stimulate the formation of novel scientific concepts and deeper understanding of a given research field, as Häse et al. suggest in their Perspective piece Designing and understanding light-harvesting devices with machine learning 7 .

We at Nature Communications are eager to continue our contribution to this exciting and fast-developing field. While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties. We strongly believe that a synergistic effort across disciplines—involving computational chemists, computer scientists, experimental chemists and material scientists—will play a crucial role for enhancing the rational design of new molecules and materials.

“While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties.”

Tkatchenko, A. Machine learning for chemical discovery. Nat. Commun. 11 , 4125 (2020).

ADS   CAS   PubMed   PubMed Central   Google Scholar  

Smith, J. S. et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10 , 2903 (2019).

ADS   PubMed   PubMed Central   Google Scholar  

Dick, S. & Fernandez-Serra, M. Machine learning accurate exchange and correlation functionals of the electronic density. Nat. Commun. 11 , 3509 (2020).

Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R. & Maurer, R. J. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat. Commun. 10 , 5024 (2019).

Liu, Y., Kilby, P., Frankcombe, T. J. & Schmidt, T. W. The electronic structure of benzene from a tiling of the correlated 126-dimensional wavefunction. Nat. Commun. 11 , 1210 (2020).

Vaucher, A. C., Zipoli, F., Geluykens, J., Nair, V. H., Schwaller, P. & Laino, T. Automated extraction of chemical synthesis actions from experimental procedures. Nat. Commun. 11 , 3601 (2020).

Häse, F., Roch, L. M., Friederich, P. & Aspuru-Guzik, A. Designing and understanding light-harvesting devices with machine learning. Nat. Commun. 11 , 4587 (2020).

PubMed   PubMed Central   Google Scholar  

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Georgia Tech will host its annual Summer Program in Theoretical and Computational Chemistry as part of its National Science Foundation Research Experiences for Undergraduates program in chemistry and biochemistry. During the ten week REU program, students will conduct research in the groups of Georgia Tech Chemistry faculty. The program is open to students who will be in their junior or senior years during the next academic year. Successful applicants will receive a stipend of $5000. More information on the Georgia Tech REU program may be found here . The Summer Program in Theoretical and Computational Chemistry supplements the normal REU research experience with a series of introductory lectures and demonstrations in theoretical and computational chemistry at the undergraduate level. Summer research projects in the areas of dynamics, statistical mechanics, and electronic structure theory are available in the groups of Professors J. C. Gumbart , Joshua Kretchmer , Jesse McDaniel , and David Sherrill . Students will additionally have access to clusters with over 44,000 compute cores to perform their research.

2021 Remote Program

Research projects, lecture series in theoretical and computational chemistry, applications.

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Department of Chemistry

Chemistry REU Program

Proposed research projects.

(Note that not all research projects and groups are available each summer. The list found here is to give a general idea of the program’s offering.)

Polymer Based Vesicles for Therapeutics Dr. Douglas Adamson (Polymer Chemistry)

Mechanistic Inorganic Chemistry Dr. Alfredo Angeles-Boza (Inorganic Chemistry)

We use synthetic chemistry, both organic and inorganic, as a tool to design and build new molecules for targeted applications. We are particularly interested in the social dilemmas of climate change and antibiotic resistance. Interestingly, both problems can be thought as examples of tragedies of the commons.

Our current research efforts are centered in two key areas: 1) Development of novel catalysts for the activation of small molecules (CO 2 , O 2 , H 2 O). We synthesize new catalysts and study their activity with a focus on kinetics and reaction mechanisms. We are one of the few groups in the world that use of heavy atom isotope effects to study reaction mechanisms. 2) Design and synthesis of compounds with medicinal properties that take advantage of the important role of metal ions in biological systems. Our approach involves synthesizing novel molecules and characterizing them with an arsenal of physical, chemical and spectroscopic data. In recent years, we have focused on the synthesis of peptides and peptidomimetics. Angeles-Boza Group Website

Use of Persistent Radical Catalysts in Living Polymerization Reactions Dr. Alexandru Asandei (Polymer Chemistry)

Synthesis and Study of DNA Damages Dr. Ashis K. Basu (Bioorganic Chemistry)

We study chemicals and drugs that exert their biological effects through DNA damage. Some of the chemicals are environmental pollutants such as 1–nitropyrene. We also study ionizing radiation-induced DNA damages. The REU student will synthesize a specific DNA damage such as a DNA adduct of a nitroaromatic compound or induce an ionizing radiation damage into a designed oligo¬deoxy¬nucleotide. These DNA lesions can induce mutations which may represent the first step converting a normal cell into a cancer cell. Our goal is to correlate the type of mutation with three dimensional architectural effects induced in DNA. The modified DNA fragments will be used to study mutagenesis and DNA repair. The project will introduce the REU student to a variety of organic synthesis and nucleic acid chemistry tools, chromatography, and structural characterization (NMR, UV-Vis, MS), and introduce the student to molecular biology and recombinant DNA techniques. Basu Web Site

Synthesis of Pyrrole-Modified Porphyrins Dr. Christian Brueckner (Organic Chemistry)

Photodynamic therapy (PDT) employs the combination of a photosensitizer, such as a porphyrin, and light to destroy diseased cells. For PDT to be most effective, the light that activates the drug must penetrate deep into tissue. However, while tissue is only transparent for red and infrared light, porphyrins cannot be activated using red light. Thus, our group has set out a program to modify synthetic porphyrins in a way that they can become photosensitizers which can be activated with red light. Although porphyrins are ubiquitous naturally occurring macrocycles, the regio-selective modification of them can be difficult. Hence, synthetic compounds are needed.

We modify a class sof symmetric meso-aryl-substituted porphyrins by formally replacing one pyrrole by a different heterocycle. One reaction sequence involves the cleavage of the ß,ß’-bond (1 to 2), followed by ring-closure to, in this example, form morpholine-derived porphyrin 3. Oxazole-, imidazole, and pyrazole-based systems are also available along this route.

The REU student will do multi-step syntheses (1-4 steps), purification (column and preparative thin layer chromatography) and characterization (UV-vis, IR, fluorescence spectroscopy, NMR) of porphyrins and metalloporphyrins (NiII, ZnII, AgII). The student will learn many analytic and synthetic techniques employed in modern organic and coordination chemistry. Brueckner Group Web Site

Modeling the Mechanisms of Light Harvesting in a Photosynthetic Antenna Protein Dr. Jose Gascon (Physical and Computational Chemistry)

Gascon Group Web Site

Automated Continuous Flow Chemistries Dr. Kerry Gilmore (Organic Chemistry)

The use of technology in chemistry allows for significant improvements in how we can study and synthesize small molecules. Most notably, the use of continuous flow techniques allows us to perform operations in a safer – and far greener – manner. This technique can be used in a wide breadth of applications, ranging from photo- and electrochemistry for more sustainable production, mechanistic studies to better understand how and why reactions occur, and the synthesis of active pharmaceutical ingredients. Coupled with machine learning, our group uses these approaches to develop better ways of making molecules and accessing previously unexplored areas of chemical synthesis. Critically, these instruments and tools need to be more broadly available, such that the entire chemical community can benefit without having to buy or build things themselves. Akin to cloud computing, we are building a network of automated instruments to perform chemical reactions – this involves writing software, automation/robotics, building new platforms, analytics, and running chemical reactions. We are looking for REU students interested in any of these areas, and those with any experience in coding/robotics are especially welcome to apply. Gilmore group website

Hybrid Materials Dr. Jie (Jay) He (Polymer Chemistry and Physical Chemistry)

Amphiphilic molecules such as liquids, surfactants, and amphiphilic block copolymers can spontaneously form a wide range of nano- or microstructures such as spherical micelles, cylindrical or worm-like micelles, or bilayer vesicles in selective solvents. Analogues to the self-assembly behaviors of atoms or molecules, the self-assembly of colloidal building blocks,so-called “colloidal molecules”, into various supra-architectures or ordered ensembles provides new opportunities to engineering structures and devices with unique optical, magnetic, or electronic properties. Our group is interested in design and synthesis of colloidal molecules and the use of colloidal molecules as model systems to understand atomic or molecular interactions in self-assembly or crystallization. The REU student will be trained with various living polymerization techniques (ATRP and RAFT polymerization) and characterization tools (NMR, GPC and electronic microscopes). The student will be exposed to the synthesis and self-assembly of various nanomaterials.

He Group Web Site

Shape-Memory Polymers Dr. Rajeswari M. Kasi (Polymer Chemistry)

We seek to synthesize, characterize, and, thereby, achieve a fundamental understanding of new biocompatible stimuli-responsive polymers. Development of new synthetic methodologies, modification of existing synthetic routes, multidisciplinary approach to structure-property evaluation, and advanced characterization tools are the overriding factors to rational material design. Shape memory polymers are a class of responsive polymers that show a reversible temporary shape change with temperature. Upon temperature reduction the initial or permanent shape is achieved once again. We are interested in exploring the influence of architecture and states of matter on shape memory application. The triggering temperature used for these applications could be the glass transition, melting or liquid crystalline transition temperature leading to a multi-variable shape memory approach, Figure 1. Shape memory polymers and hybrid structures can be used in drug delivery, tissue engineering scaffolds, artificial muscles, and actuators.

The undergraduate student researcher will be mentored by a graduate student and the faculty member. The student will learn synthetic polymer chemistry methods and characterization techniques to investigate stimuli-responsive and shape memory properties. Kasi Group Web Site

Synthesis and characterization of photoswitchable inhibitors of potassium channels Dr. Michael Kienzler (Organic and Biological Chemistry)

Potassium channels are essential proteins for maintaining excitable cells’ membrane potential, perhaps best known for their role in neuron action potential firing.  New molecular tools are needed to interrogate the function of potassium channels with high spatiotemporal precision.  Our lab is interested in synthesizing small, photoswitchable molecules that can be used to control protein function, and in this project in particular, potassium channel blockers that can be turned “on” and “off” with different wavelengths of light.  To achieve this goal, our photoswitch of choice is azobenzene, which can isomerize between  cis   and   trans   forms by irradiating the molecule with different wavelengths of light in the Ultraviolet/visible range.

The REU student will synthesize a series of azobenzene-based photoswitchable inhibitors of potassium channels (2-5 steps), purifying (via column chromatography and HPLC) and characterizing (NMR, Mass Spec) their compounds as they go.  The photochemical properties of the final compounds will also be determined (UV/vis spectroscopy, NMR).

From the Kitchen to the Lab Dr. Nicholas Leadbeater (Inorganic Chemistry)

We all know that microwave ovens can be used for heating food fast. An exciting area of study in the synthetic chemistry community is the use of microwaves for making molecules rapidly, easily and cleanly. Using microwave heating, it is possible to enhance the rate of chemical reactions significantly and to do chemistry that was otherwise not possible. Unlike the microwave at home, we use state-of-the-art scientific microwave systems that allow precise control of reaction conditions. One limitation at the moment is the scale-up of reactions to make multi-gram or kilo quantities of compounds. However, we are about to receive a microwave apparatus that is designed to overcome this hurdle. As an REU student, you would play an important role in using this apparatus over the summer and would have your own mini-project focused around the use of microwave heating for scaling-up reactions. You will be mentored by a graduate student in the group. The reactions will be performed in water as a solvent rather than organic solvents thus making the chemistry more environmentally friendly. As well as being exciting, the project will introduce you to a range of modern synthetic chemistry techniques as well as analysis methods. Leadbeater Group Web Site

Supramolecular Assembly of Polypeptides into Nanomaterials Dr. Yao Lin (Polymer Chemistry)

Control of photo-generated charge-separated states in donor-bridge-acceptor molecules dr. tomoyasu mani (physical chemistry).

Research in the Mani Group focuses on photo- and radiation-induced fundamental chemical reactions in the condensed phase. We are particularly interested in controlling electronic excited states, charge and exciton transfer reactions, and spin dynamics in molecules and molecular assemblies. The fundamental understanding of these phenomena will help us improve and develop energy and biomedical technologies.

The REU student will work on the projects that examine the way(s) to control photo-generated charge-separated states. Students will have an opportunity to do either or both organic synthesis and optical (both steady-state and time-resolved) spectroscopy experiments.

Mani Group Web Site

Nanoscale Controlled Light Emitting Devices by Self-Assembly Techniques Dr. Fotios Papadimitrakopoulos (Polymer Chemistry)

Implantable biosensors could be a plausible way to continuously monitor blood glucose levels, provided they exhibit long-term stability and means to establish telemetry. However, their potential applications remain largely unexploited due to the negative tissue responses such as biofouling, inflammation, tissue fibrosis, and calcification generated by the implantation of such devices. Other problems such as electrical short, signal drifts and need for continuous calibration can lead to device malfunctioning and eventually failure. Also, one of the chief concerns is the possibility of sensor breakdown because of oxidative degradation of enzyme and other electrode coatings due to excess of hydrogen peroxide present in the immediate vicinity of the sensing electrode. This is a direct result of over-sampling of the glucose in the blood stream. Coating the device by a biocompatible, semipermeable membrane can rectify this situation. Apart from acting as a barrier to permeation of glucose, the membrane would protect the sensor from foreign molecules that cause fouling. Our group investigated the simplistic, yet versatile approach of layer-by-layer (LBL) self-assembly of assembly of Humic Acids (Has), a naturally occurring biopolymer and Fe3+ cations. Not only did these coatings provide the required degree of glucose permeability, but in vivo results indicated their biocompatibility with reduced tissue fibrosis upon implantation. Furthermore, the conformation and growth characteristics of the HAs/Fe3+ membrane could be tailored by carefully adjusting the pH of the aqueous medium. Apart from the HAs/Fe3+ bilayers, we self-assembled films of HAs/poly (diallyldimethylammonium chloride) (PDDA) and also films of poly (styrene sulfonate) (PSS)/PDDA onto the sensory device. Moreover the diffusion coefficients of glucose through these membrane systems were investigated in order to explain the individual sensor response as it pertains to the microstructure of these outer semipermeable membranes. The hysterisis behavior of these sensors was studied as a function of permeability of the outer membrane. It was concluded that the microstructure of these coatings govern the permeability of glucose and correspondingly, the sensitivity, longevity and hysterisis of the sensors. We plan to extend this outer membrane research to a more biocompatible polyelectrolytes like poly saccharides and proteins, which we aniticipate to finish within one summer.

The incoming REU student will be exposed to a variety of techniques including electrochemical sensor fabrication, electro-analytical techniques, ellipsometry, enzyme immobilization, electropolymerization of conducting polymers, layer by layer assembly, in vitro and in vivo testing of electrochemical sensors as well diffusional based theoretical modeling of electrochemical sensors. Papadim. Group Web Site

Synthesis as a Tool in Glycoscience Dr. Mark W. Peczuh (Organic Chemistry)

Carbohydrates are indispensable to biological processes such as metabolism, protein folding, and cell-cell interactions. Our group is interested in the design, synthesis, and characterization (conformation, binding) of ring expanded carbohydrates that can interact with natural proteins such as lectins and glycosidases. The preparation of novel ligands of these two broad groups of carbohydrate binding proteins may provide new tools for glycobiology or even future drug leads.

The REU student will synthesize septanose carbohydrate glycosides and glycoconjugates designed for their ability to bind natural lectins and glycosidases. The routes for their synthesis will rely on established procedures, or will be developed by the student. They will be multistep sequences (4-6 steps), where compound purification (chromatography, crystallization) and spectroscopic characterization (NMR, IR, CD, MS) are critical aspects of the research. Peczuh Group Web Site

Building Functional Nanodevices with Porous Nanocapsules Dr. Eugene Pinkhassik (Materials/Organic/Analytical/Nanoscience)

Our research group designs functional nanomaterials and devices with new and superior properties to address global challenges in energy-related technologies, sensing, and medical imaging and treatment. We have developed a directed assembly method for the synthesis of vesicle-templated nanocapsules. These nanocapsules offer a unique combination of properties enabled by robust shells with the single-nanometer thickness containing programmed uniform pores capable of fast and selective mass transfer. Vesicle-templated nanocapsules emerged as a versatile platform for creating functional devices, such as nanoreactors, nanosensors, and containers for drug delivery.

The REU student will learn an array of synthetic and analytical techniques ranging from the synthesis of polymer nanocapsules, using self-assembled structures to direct organic synthesis, characterizing nanoscale objects with light scattering and electron microscopy, and evaluating the performance of newly created nanodevices with spectroscopic and chromatographic methods. Having mastered the synthesis of nanocapsules, the REU student will use the capsules to build nanodevices aiming at one of the following applications: nanoreactors with encapsulated homogeneous or enzymatic catalysts, highly selective nanoprobes, containers for the delivery of drugs or imaging agents, or cell-mimicking devices capable of through-shell communication.

Pinkhassik Group Web site

Reliability and Engineering of Molecules and Materials Next-generation Electronics. Dr. Rebecca Quardokus (physical/materials/nanoscience)

The Quardokus group focuses on the reliability and engineering of molecules and new materials for next-generation electronics. Scanning tunneling microscopy (STM), with its ability to image individual atoms and molecules, is the primary tool used to investigate surface-confined molecular interactions and two-dimensional materials.  The systems of interest include self-assembled monolayers, two-dimensional polymers, surface-confined reactions, hierarchical designs, and surface-confined molecular rotors and switches. Quardokus Group Web Site

Enzyme-assembled Nanocapsules for Targeted Drug Delivery Dr. Jessica Rouge (Biological Chemistry)

We seek to design, synthesize and characterize nanomaterials that can target specific cell types for the delivery of therapeutic nucleic acids and small molecule drugs.

Nanomaterials have revolutionized the way drugs can be delivered thanks to their small size and enhanced chemical stability. However the ability to direct them to specific cellular targets and to control the release of their therapeutic cargo has been a major obstacle in the field. Our lab seeks to develop new materials that can direct the localization of a nanomaterial to specific cell receptors through the use of DNA aptamers. Aptamers are DNA and RNA sequences that strongly bind specific cellular locations or proteins. We are also interested in controlling the release of the nanomaterials contents through interactions with specific enzymes (esterases). We work to synthesize new substrates that can direct enzymes to the surface of nanomaterials in order to facilitate the enzyme-mediated assembly of chemically modified aptamers to particle surfaces along with the degradation of the nanomaterials itself.

An undergraduate researcher will be exposed to a highly interdisciplinary lab environment, being trained by both a graduate student and the faculty member. The students will learn both chemical and biochemical techniques such as nanoparticle synthesis, automated DNA synthesis, HPLC, PCR, RNA transcription and other enzymatic reactions. Rouge Group Web Site

Cancer Biomarker Detection by Immunoarrays Dr. James F. Rusling (Analytical, Physical Chemistry)

The student will develop analytical protocols for these analyses in serum samples, and attempt to improve sensitivity, detection limit and reproducibility compared to our existing arrays. The student will learn state-of-the-art biomedical sensor preparation technology utilizing nanoparticles and ink-jet biomolecule spotting. The student will also gain experience in electrochemical, AFM and spectroscopic analyses to monitor array fabrication, and the use amperometry for biomarker detection with the microfluidic arrays. Rusling Group Web Site

Catalysts, Ceramics, Batteries, and Adsorbents Dr. Steven L. Suib (Inorganic Chemistry)

Departments of chemistry, chemical engineering, and materials science and engineering, and institute of materials science..

Our NSF funded research program involves the preparation of aligned crystallites on solid surfaces that can be used as Catalysts, Ceramics, Batteries, and Adsorbents. Much of this research involves synthesis of novel metal oxide and sulfide materials that are densely packed but accessible to chemical reagents for distinct chemical and physical reactions.

Figure 1, Diagram of Oriented Fibers; Synthesis, Scanning Electron Micrograph and Coated Product.

Figure 1 above shows a diagram of one of the synthetic processes that is used to make such oriented crystallites. These nano-sized materials are shown to be well aligned in the scanning electron micrograph shown above. The photograph on the right in Figure 1 is that of an uncoated (cream color) cordierite monolith like that in an auto exhaust system in cars and the coated (dark brown) honeycomb support with aligned crystallites. A major advantage of the alignment is that more accessible sites are available for whatever the specific application might be.

For example, the materials in Figure 1 are being studied as auto exhaust catalysts and have shown excellent activity and stability in the oxidation of CO and the reduction of NO x . the same types of oriented materials can enhance the capacity of battery materials, and increase the amount of adsorption for example of extracting harmful sulfur and nitrogen species from a variety of fuels. Many of these materials act as ceramic systems that are stable at very high (> 500 o C) temperatures.

The type of research that would be done under an REU summer program would involve any aspect of synthesis, characterization, or applications of such oriented materials. Related goals of this research program involve use of green reagents, regeneration and sustainability of systems, and scale-up of materials and processes.

References.

Chen, S. Y.; Song, W.; Lin, H. J.; Wang, S.; Biswas, S.; Mollahosseini, M.; Kuo, C. H.; Gao, P. X.; Suib, S. L.; Manganese Oxide Nano-Array Based Monolithic Catalysts: Tunable Morphology and High Efficiency for CO Oxidation, ACS Appl. Mat.  & Int ., 2016, 8 , 7834-7842.

Dutta, B.; Biswas, S.; Sharma, V.; Savage, N. O.; Alpay, S. P.; Suib, S. L., Mesoporous Manganese Oxide Catalyzed Aerobic Oxidative Coupling of Anilines to Aromatic Azo Compounds, Ang. Chem. Int. Ed ., 2016, 55 , 2171-2175.

Synthesis of molecules emitting chiral light Dr. Gaël Ung (Inorganic/organic)

Circularly polarized luminescence (CPL) is the preferential emission of light with a certain circular polarization. Upon non-polarized light absorption, a chiral molecule reaches a preferential excited state which radiatively decays by emitting circularly polarized photons. CPL has emerged as a next-generation light source since the added chiral optical information presents unique opportunities to enhance optical displays, bio-imaging, and security f eatures for banknotes and identification documents. The REU student will synthesize chiral and enantiopure ligands, and study their coordination to lanthanides. The complexes obtained should exhibit CPL. Our laboratory is equipped with two rare CPL spectrometers, including the only NIR-CPL in the Americas. The REU student will be trained in a large variety of synthetic techniques (bench top, Schlenk, glove box), as well as spectroscopic characterizations (NMR, UV-vis, IR, EPR, CPL).

Ung Group Web Site

Mass Spectrometry to Investigate Micro-Scale Preparation of Peptide Samples Dr. Xudong Yao (Analytical Chemistry and Biological Chemistry)

Mass spectrometry is used as a fast and sensitive tool to study peptides. Mass spectrometry analyzes charge-to-mass ratios of peptide ions in gas phase. A mass spectrum plots the intensities of ions against their charge-to-mass ratios. These ratios can be used to determine chemical structures of peptides, while the intensities give relative quantitation of the ions. Sample preparation of peptides is a key step for successful mass spectrometric analysis, and it is often done at a micro-scale. In REU summer projects, students will work on different sample manipulations of peptides such as chemical modification of peptide mixtures and use mass spectrometry to study the efficiency of various micro-scale procedures for peptide sample preparation. The REU project will specifically investigate analytical challenges in mass spectrometric analysis of phosphopeptides. Phosphopeptides are fragments of phosphoproteins that are important regulators for cellular signaling. Analysis of protein phosphorylation is important to understand and treat various human diseases and to manipulate the fate of stem cells for therapeutic and regenerative applications. The REU researcher will study ß-elimination and Michael addition reactions of phosphopeptides. Objectives of the project are to minimize side reactions and maximize the efficiency of the sample preparation workflow that will be examined by high performance liquid chromatography and tandem mass spectrometry. Yao Group Web Site

Synthesis and application of metal and semiconductor nanoparticles Dr. Jing Zhao (Analytical and Physical Chemistry)

Chemical Science

From computational screening to the synthesis of a promising oer catalyst †.

* Corresponding authors

a Department of Chemistry, University of Toronto, Toronto, Canada E-mail: [email protected]

b Department of Computer Science, University of Toronto, Toronto, Canada

c Catalonia Institute for Energy Research, Barcelona, Spain

d Department of Materials Science and Engineering, University of Toronto, Toronto, Canada

e Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

f Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, China

g State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

h Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

i Department of Chemical Engineering & Applied Chemistry, University of Toronto, Canada

j Vector Institute for Artificial Intelligence, Toronto, Canada

k Canadian Institute for Advanced Research (CIFAR), Toronto, Canada

l Acceleration Consortium, University of Toronto, Toronto, Canada

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co 2.5 Ga 0.5 O 4 , that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm −2 and a Tafel slope at 56.0 mV dec −1 . Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts.

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From computational screening to the synthesis of a promising OER catalyst

S. G. Hari Kumar, C. Bozal-Ginesta, N. Wang, J. Abed, C. H. Shan, Z. Yao and A. Aspuru-Guzik, Chem. Sci. , 2024, Advance Article , DOI: 10.1039/D4SC00192C

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Saint Michael’s students kick off on-campus research projects for summer 2024

On Tuesday, June 4, students and professors gathered in the Roy Room to offer some insight into their summer research projects. From student-athlete mental health and historical beliefs about crime to immunoprecipitation and environmental paleolimnology, students are performing important research on a variety of topics and questions facing the world today.

Many of the projects are funded by grants from the Vice President for Academic Affairs Office, while some are funded through outside grants, including through the Vermont Biomedical Research Network. Students and their faculty mentors apply for the grant funding and work together throughout the summer to complete the research. Each student will present their research findings during the upcoming academic year.

Below are descriptions of each research project happening on campus during the summer of 2024.

“How does Farm Consolidation around the United States impact Climate Change?”

Benjamin Riggs ’26

Major: Business Administration

Mentor: Professor Patrick Walsh (Economics)

Riggs is conducting research on farm consolidation around the U.S., exploring how it impacts climate change. Over eight weeks, Riggs will compare data on water use and the number and size of farms, analyzing the environmental impacts of farming as the country shifted from small farming practices to sizable commercial farming. Riggs aims to find conclusions on whether farm consolidation across the U.S. has led to increased water use per acre and the environmental impacts this may have on climate change. 

Elsa Gordon ’27 explains her summer research project during a research kickoff event on June 4, 2024. (Photo by Cat Cutillo/Saint Michael’s College)

Research collaboration with UVM SOCKS

Elsa Gordon ’27

Majors: Data Science and Environmental Studies

Mentor: Professor Candas Pinar (Sociology)

In collaboration with research done by the University of Vermont SOCKS (The Science of Online Corpora, Knowledge, and Stories), Gordon will gather and analyze text data from different sources, including Governor Phil Scott’s press conferences, and Vermont-specific Twitter and Reddit data. “The SOCKS team has developed a set of computational tools to measure the sentiment and stories across a diverse pool of written texts,” Gordon said. Through these tools and other qualitative research methods, Gordon aims to measure Vermonters’ sense of community or connectedness during the COVID-19 pandemic. 

Innovative ways to measure blue-green algae

Shannah Weller ’27

Major: Biology, minor in Physics

Mentor: Professor Clay Williams (Environmental Studies and Science)

Professor Williams has designed a machine that will help monitor blue-green algae levels in nearby bodies of water. In collaboration with the University of Vermont, Weller will be part of the construction of some parts of this machine over the summer, along with collecting samples at five different Lake Champlain sites. In the end, Williams and Weller want to find a better way to spot the signs of blue-green algae blooms before they are dangerous to the public. “Green algae produces toxins, and is often the cause of beach closures over the summer months, posing a risk for people, pets, and more,” Weller said. “The goal is to make a more efficient, affordable, and 24/7 way to monitor blue-green algae levels because as of now, it is mostly done through volunteer efforts.” 

Alexa Roux ’26 explains her summer research project with Professor Clay Williams during a summer research kickoff event on June 4, 2024. (Photo by Cat Cutillo/Saint Michael’s College)

“Environmental Paleolimnology in Saint Michael’s Natural Area Waterbodies”

Alexa Roux ’26

Majors: Chemistry and Environmental Science

Through testing of four freshwater bodies in the natural area, Roux’s research will use paleo-ecotoxicology to determine how past anthropogenic contamination in the aquatic sediment influences the current water quality. “Because natural freshwater bodies are often at the lowest point of elevation, all of the man-made contaminants run through flooding and precipitation into these lakes. These contaminants build up in layers, which can be indicators of the lake’s health,” Roux said. Roux will compare the current state of the water to the historical record of its sediment. 

“Can Geographical Informational Systems (GIS) Enabled Mapping Accelerate Organic Farm Certification?”

Preston Hewett ‘25

Major: Environmental Science 

In collaboration with the Northeast Organic Farming Association of VT (NOFA-VT), Hewett will be transforming paper maps into interactive maps using geographical informational systems.  The goal is to make the maps more accurate, spatially aware, and dynamic. “My personal goal is to further grow these organic operations, because even though it is more expensive than conventional farming, it is a better, sustainable way to go in the future,” Hewett said. This research will test the feasibility in using dynamic maps during federal organic farm inspections, lessening the time taken to verify organic production practices.

View photos of the summer research kickoff event by Cat Cutillo, below:

“The Politics of Climate Change and Human Migration”

Olivia Francisco ‘25

Majors: Environmental Studies and Political Science 

Mentor: Professor Shefali Misra (Political Science)

Francisco will investigate how climate change has had an impact on political stability and international relations through human migration patterns in Central and Eastern Africa. Francisco plans to analyze quantitative and content migration data, in addition to political discourse on environmental migration. This study aims to give insight into how vulnerable populations are affected by displacement in the context of climate change-related crises. 

Colby Fane-Cushing ’25 describes his ongoing research that he plans to continue during the summer of 2024 during a research kickoff event on June 4, 2024. (Photo by Cat Cutillo/Saint Michael’s College)

Studying stress in student-athletes

Colby Fane-Cushing ‘25

Major: Neuroscience

Mentors: Professor Melissa VanderKaay Tomasulo (Psychology) and Professor Dagan Loisel (Biology)

For the past year, Fane-Cushing worked on a questionnaire research study, asking student-athletes a variety of questions related to their own perceptions of their stress and anxiety levels. Findings from this questionnaire will be used for a longitudinal study that tests the impact of virtual reality meditation on student-athletes’ stress levels. Over the summer, Fane-Cushing will be working on an institutional review board proposal, so that by the time the fall semester begins, his future research project can launch. “In past research projects with Professor Tomasulo and Loisel, VR meditation has been shown to be successful in reducing self-perceived stress,” Fane-Cushing said. With this project, Fane-Cushing aims to replicate these results, this time with student-athlete participants. Both self-perception and biological measures, such as heart rate and blood pressure, will be used in this study. As a student-athlete himself, Fane-Cushing said, “It is important that our student-athletes can perform at their best and our study will hopefully show that training the mind is as important as training the body.”

“New, Naturally-Derived Food Preservatives”

Ken Zou ‘26

Major: Chemistry

Mentor: Professor Mark Scialdone (Chemistry)

Zou is working with THC and CBD strands and hopes that by the end of the project, they can be used as natural preservatives. Zou is using strawberries and bagels to test this possibility, observing how long mold takes to form on these foods. “Ken did six compounds in just two weeks, which already exceeded our expectations,” Scialdone said. 

“Developing and Running an Immunoprecipitation Assay for the Binding Partners of X-MAID Moesin in T cells”

Gavin Graham  ‘25

Major: Biochemistry

Mentor: Professor Lyndsay Avery (Biology)

X-Linked Moesin Associated Immunodeficiency (X-MAID) is a genetic disease associated with lymphopenia, neutropenia, and bacterial infections, affecting T cells and T cell migration. Graham will develop an immunoprecipitation assay to better understand one of the specific mutations of moesin, and how it can affect T cell function. He hopes that a better understanding the mutation will help in effectively treating the X-MAID disease and mitigate patients’ symptoms.

“Analyzing the Effect of X-MAID Mutant Moesin Expression in T cell During Mitosis”

Olivia Goldfarb ‘27

Major: Biology

Goldfarb will study how the X-MAID mutation of T cells affects the final stage of mitosis (cell division), or telophase, as compared to a control group of healthy moesin. A healthy moesin protein allows for the effective division of T cells. Understanding these underlying mechanisms can help provide better insights into the role of moesin in T cell biology. X-MAID is a serious immunodeficiency illness, and with this research, Goldfarb hopes her findings will aid the treatment of X-MAID, having a direct impact on patients, and improving their quality of life. 

“Perception and Purpose: A Catalog of the Modern History of Israelis and Palestinians”

Isabella Cronin ’25

Major: International Relations

Mentor: Professor M.J. Bosia (Political Science, International Relations, Gender & Sexuality Studies)

After studying abroad in Jordan, Cronin paid close attention to regional media as violence between Israel and Gaza escalated last fall. She compared those narratives to Western media and found different narratives. With this research, Cronin aims to pull together Israeli and Palestinian views on major historical events, such as the Nakba. Cronin aims to create an online catalog defining terms such as “genocide,” “settler colonialism,” “Zionism,” “diaspora,” “Palestinian liberation,” “right to defense,” and more. Cronin will be working on an interactive catalog with descriptions, definitions, historical context, a timeline of events, and key figures.

“Transcribing Early American Documents Relating to the Founding of the Virginia State Penitentiary”

Julianne Giordano ‘27

Majors: History and Secondary Education

Mentor: Professor Alexi Garrett (History)

Giordano is transcribing Virginia State Penitentiary documents, such as letters and criminal records dating back to the first two decades of the 1800s, which are related to Professor Garrett’s research. In this research, she will be paying special attention to indications of racial and gender bias within the prison. Giordano will be translating information about convicted criminals into charts, and produce a cumulative paper. Findings will provide research for a chapter of Professor Garrett’s book, and to co-write a journal article. 

“How do College Campuses Respond to Students’ Mental Health Crises?”

Damien Wortheim ‘26

Majors: Psychology and Equity Studies

Mentor: Professor Sarah Nosek (Psychology) 

For this research, Wortheim will explore how college campuses support student mental health through their apporach to crisis intervention. By performing content analyses on colleges’ response protocols from several departments, Wortheim’s research will support Clinical-Counseling Psychology graduate student Kaitlyn Root’s master thesis on crisis intervention on college campuses. Wortheim will be in charge of early stage research, such as content analysis and archival research, looking at how different universities’ approach mental health crises. 

“The Other Side of the Story: Healthcare Providers’ Perspectives on Chronic Pain”

Lauren Welch ‘25

Major: Psychology

Mentor: Professor Sarah Nosek (Psychology)

Through interviews with healthcare providers, Welch will explore their perspectives on interacting with and treating chronic pain patients. Topics of exploration include perceptions of chronic pain, ways of communicating with patients, and challenges of managing and treating chronic pain. Welch expects her findings will show that the relationship between chronic pain patients and their healthcare providers is impacted by both interpersonal factors and systemic challenges. With this research, Welch hopes to uncover important issues that if addressed, could greatly improve well-being both for patients and providers.

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UC Irvine Engineering Graduate Program Rises in U.S. News Rankings  

June 18, 2024 - The UCI Samueli School of Engineering has received its highest ranking ever in the U.S. News & World Report’s 2024-25 list of best graduate schools. The school is continuing the upward trend over the past years and is currently ranked 17th among the nation’s public universities and 31 overall among public and private institutions. This represents an increase of three places compared to last year’s rank.

Each year, U.S. News surveys graduate programs in the areas of business, education, engineering, law and medicine. This year, 199 engineering schools that grant doctoral degrees responded to the survey. The annual rankings are based on peer assessments of program excellence as well as statistical indicators that measure the quality of a school's faculty, research and students.

“It is very encouraging to see our graduate program continue to climb in the rankings,” said Magnus Egerstedt, dean of engineering. “It is a testament to the hard work and dedication of our faculty, students and staff and the increasing impact of our research programs. I’m very proud of our progress and am looking forward to the future as we continue to pursue new and exciting ventures.”  

Six engineering specialty programs saw improvements in rankings including aerospace engineering with a jump of nine places to 16th among publics, and environmental engineering with a rise of six places to 22nd among publics. The other programs that saw positive movement were biomedical engineering, chemical engineering, electrical engineering and mechanical engineering.

– Lori Brandt

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