Computational Chemistry and Theory

People

Computational chemistry employs quantum and classical approaches to simulate molecular properties and dynamics of chemical systems. C-CAS researchers utilize computer-based techniques (ex. molecular dynamics, coupled cluster, DFT) to study reaction mechanism, develop chemical descriptors, and generate data for training new machine learning models.

Publications

  • Torres, Jose Antonio Garrido; Lau, Sii Hong; Anchuri, Pranay; Stevens, Jason M; Tabora, Jose E; Li, Jun; Borovika, Alina; Adams, Ryan P; Doyle, Abigail G. A Multi-Objective Active Learning Platform and Web App for Reaction Optimization. J. Am. Chem. Soc. 2022, 144,43,19999-20007. https://pubs.acs.org/doi/10.1021/jacs.2c08592

  • Żurański, A.M.; Wang, J.Y.; Shields, B.J.; Doyle, A.G. Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules. React. Chem. Engin. 2022, 7, 1276-1284  https://doi.org/10.1039/D2RE00030J

  • Luchini, Guilian, and Robert Paton. "Bottom-up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters." ChemRxiv (2023) 10.26434/chemrxiv-2023-n8jsm-v2.

  • Santhanalakkshmi Vejaykummar, S. S.; Kim, Y.; Kim, S.; St. John, P.; Paton, R. Expansion of Bond Dissociation Prediction with Machine Learning to Medicinally and Environmentally Relevant Chemical Space.  ChemRxiv 2023. 10.26434/chemrxiv-2023-d147w

  • Luchini, G., Alegre-Requena, J. V., Funes-Ardoiz, I., Paton, R.S. GoodVibes: Automated thermochemistry for heterogeneous computational chemistry data. F1000Research, 2020, 9, 291. https://doi.org/10.12688/f1000research.22758.1

  • Gallegos, L.C.; Luchini, G.; St John, P.C.; Kim, S.; Paton, R.S. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties Acc. Chem. Res. 2021, 54, 4, 827-836.   https://pubs.acs.org/doi/10.1021/acs.accounts.0c00745

  • Yang, Y.; Zhang, S.; Ranasinghe, K.; Isayev, O.; Roitberg, A. “Machine Learning of Reactive Potentials.” ChemRxiv. 10.26434/chemrxiv-2023-x82fz.

  • Guan, Y.; Sowndarya. S.S.; Gallegos, L.C.; St. John, P.C.; Paton, R.S., Real-Time Prediction of 1H and 13C Chemical Shifts with DFT Accuracy Using a 3D Graph Neural Network. Chem. Sci. 2021, 12, 12012-12026.   https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc03343c

  • Jones, K.E.; Park, B.; Doering, N.A.; Baik, M.H.; Sarpong, R.  Rearrangements of the Chrysanthenol Core: Application to a Formal Synthesis of Xishacorene B. J. Am. Chem. Soc. 2021, 143, 20482–20490 https://doi.org/10.1021/jacs.1c10804

  • Fedik, N.; Nebgen, B.; Lubbers, N.; Barros, K.; Kulichenko, M.; Li, Y.W.; Zubatyuk, R.; Messerly, R.; Isayev, O.; Tretiak, S. "Synergy of semiempirical models and machine learning in computational chemistry." J. Chem. Phys. 2023, 159, 110901.  https://pubs.aip.org/aip/jcp/article/159/11/110901/2911476

  • Liu, Zhen, Yurii S. Moroz, and Olexandr Isayev. "The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions." Chem.  Sci. 2023, 14, 10835-10846.  doi https://doi.org/10.1039/D3SC03902A