data_driven_chemistry

Data-Driven Chemistry

Like most scientists, chemists are drowning in data from laboratory experiments and from calculations. We are developing tools using machine learning to automate the analysis of quantum-chemistry. Another area in need of automation is in the development of quantitative structure-property relationships, particularly where flexible molecules are concerned.

Collaborators

Matt Sigman (Utah), Tom Rovis (Columbia); Steven Fletcher (Oxford)

Key Papers

Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines.

Haas, B. C.; Hardy, M. A.; Sowndarya S. V., S.; Adams, K.; Coley, C. W.; Paton, R. S.; Sigman, M. S. in revision 2024, DOI: 10.26434/chemrxiv-2024-m5bpn

Predicting Lewis Acidity: Machine Learning the Fluoride Ion Affinity of p-Block Atom-based Molecules.

Sigmund, L. M.; Sowndarya, S. S. V.; Albers, A.; Erdmann, P.; Paton, R. S.; Greb, L. Angew. Chem. Int. Ed. 2024, DOI: 10.1002/anie.202401084

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters.

Luchini, G.; Paton, R. S. ACS Phys. Chem. Au2024, 4, 259–267

Expansion of Bond Dissociation Prediction with Machine Learning to Medicinally and Environmentally Relevant Chemical Space.

Sowndarya, S. S. V.; Kim, K.; Kim, S.; St. John, P. C.; Paton, R. S. Digit. Discov. 2023, 2, 1900-1910.

Combining mechanistic and statistical models for predicting reaction outcomes in organic synthesis.

Gallegos, L. C. Colorado State University 2023

Regiodivergent Nucleophilic Fluorination under Hydrogen Bonding Catalysis: A Computational and Experimental Study.

Horwitz, M. A.; Dürr, A. B.; Afratis, K.; Chen, Z.; Soika, J.; Christensen, K. E.; Fushimi, M.; Paton, R. S.; Gouverneur, V. J. Am. Chem. Soc. 2023, 145, 9708–9717

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries.

Sowndarya, S. S. V.; Law, J.; Tripp, C.; Duplyakin, D.; Skordilis, E.; Biagioni, D.; Paton, R. S.; St. John, P. C. Nat. Mach. Intell. 2022, 7, 720–730

Mechanistic Studies Yield Improved Protocols for Base-Catalyzed anti-Markovnikov Alcohol Addition Reactions.

Luo, C.; Alegre-Requena, J. V.; Sujansky, S. J.; Pajk, S.; Gallegos, L. C.; Paton, R. S.; Bandar, J. S. J. Am. Chem. Soc. 2022, 144, 9586–9596

Homologation of Electron-Rich Benzyl Bromide Derivatives via Diazo C–C Bond Insertion.
Modak, A.; Alegre-Requena, J. V.; Lescure, L.; Rynders, K. J.; Paton, R. S.; Race, N. J. Am. Chem. Soc. 2022, 144, 86–92
A Quantitative Metric for Organic Radical Persistence Using Thermodynamic and Kinetic Features.

Sowndarya, S. S. V.; St. John, P. C.; Paton, R. S.Chem. Sci. 2021, 12, 13158-13166.