| A foundation model for atomistic materials chemistry I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096, 2023 | 71 | 2023 |
| Seasonal changes in diet and chemical defense in the Climbing Mantella frog (Mantella laevigata) NA Moskowitz, AB Roland, EK Fischer, N Ranaivorazo, C Vidoudez, ... PLoS One 13 (12), e0207940, 2018 | 28 | 2018 |
| Team-based learning for scientific computing and automated experimentation: visualization of colored reactions S Vargas, S Zamirpour, S Menon, A Rothman, F Häse, ... Journal of Chemical Education 97 (3), 689-694, 2020 | 27 | 2020 |
| Machine learning to predict Diels–Alder reaction barriers from the reactant state electron density S Vargas, MR Hennefarth, Z Liu, AN Alexandrova Journal of chemical theory and computation 17 (10), 6203-6213, 2021 | 24 | 2021 |
| Computational and Experimental Design of Quinones for Electrochemical CO2 Capture and Concentration AM Zito, D Bím, S Vargas, AN Alexandrova, JY Yang ACS Sustainable Chemistry & Engineering 10 (34), 11387-11395, 2022 | 11 | 2022 |
| Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field ANA Shobhit S. Chaturvedi, Santiago Vargas, Pujan Ajmera Journal of the American Chemical Society, 2024 | 3 | 2024 |
| High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties AA Santiago Vargas, Winston Gee Digital Discovery, 2024 | 2* | 2024 |
| An Artificial Intelligence Framework for Optimal Drug Design G Ramey, S Vargas, D De Alwis, AN Alexandrova, J Distefano III, ... bioRxiv, 2022.10. 29.514379, 2022 | 2 | 2022 |
| Machine-learning prediction of protein function from the portrait of its intramolecular electric field S Vargas, S Chaturvedi, A Alexandrova | 1 | 2024 |
| HEPOM: A predictive framework for accelerated Hydrolysis Energy Predictions of Organic Molecules RD Guha, S Vargas, EWC Spotte-Smith, AR Epstein, MC Venetos, M Wen, ... AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 0 | 1 | |
| Thermodynamic Equilibrium versus Kinetic Trapping: Thermalization of Cluster Catalyst Ensembles Can Extend Beyond Reaction Time Scales P Poths, S Vargas, P Sautet, AN Alexandrova ACS Catalysis 14 (7), 5403-5415, 2024 | | 2024 |
| Geometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization S Vargas UCLA, 2024 | | 2024 |
| Prediction of Reaction Orthogonality using Machine Learning H Roshandel, S Vargas, A Lai, A Alexandrova, P Diaconescu | | 2023 |
Anastassia N. AlexandrovaChemistry and Biochemistry, UCLA在 chem.ucla.edu 的电子邮件经过验证
Anastassia N. AlexandrovaChemistry and Biochemistry, UCLA在 chem.ucla.edu 的电子邮件经过验证