Alumni Profiles

Santiago Vargas

  • Program Years: 2020-2024
  • Academic Institution: University of California, Los Angeles
  • Field of Study: Theoretical and Computational Chemistry
  • Academic Advisor: Anastassia Alexandrova
  • Practicum(s):
    Lawrence Berkeley National Laboratory (2022)
  • Degree(s):
    B.A. Chemistry and Physics, Harvard College, 2019
    PhD. Computational and Theoretical Chemistry, UCLA, 2024

Current Status

  • Research Area: Theoretical and Computational Chemistry

Publications

Machine-learning prediction of protein function from the portrait of its intramolecular electric field (2024), S. Vargas*, S. Chaturvedi, A. N. Alexandrova. Journal of the American Chemical Society, 10.1021/jacs.4c09549.

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field (2024), S. Chaturvedi, S. Vargas*, P. Ajmera, A. N. Alexandrova. Journal of the American Chemical Society, 10.1021/jacs.4c03914.

High-throughput Quantum Theory of Atoms in Molecules (QTAIM) Applied to Geometric Deep Learning. S. Vargas*, W. Gee, A. N. Alexandrova. Digital Discovery, 2024, DOI: 10.1039/D4DD00057A.

A foundation model for atomistic materials chemistry. I. Batatia …, S. Vargas, … (10.48550/arXiv.2401.00096, in preparation)

Thermodynamic Equilibrium versus Kinetic Trapping: Thermalization of Cluster Catalyst Ensembles Can Extend Beyond Reaction Time Scales. P. Poths, S. Vargas*, P. Sautet, and A. N. Alexandrova. ACS Catalysis 0, 14. 10.1021/acscatal.3c06154.

HEPOM: A predictive framework for accelerated Hydrolysis Energy Predictions of Organic Molecules (2023) R. D. Guha, S. Vargas*, E. W. C. Spotte-Smith, A. R. Epstein, M. C. Venetos, M. Wen, R. S. Kingsbury, S. M. Blau, K. A. Persson( https://openreview.net/forum?id=eDlEn1PPJw).

An Artificial Intelligence Framework for Optimal Drug Design (2022) G. Ramey, S. Vargas*, Dinesh De Alwis, Anastassia N. Alexandrova, Joe Distefano III, Peter Bloomingdale bioRxiv 2022.10.29.514379. 10.1101/2022.10.29.514379.

Computational and Experimental Design of Quinones for Electrochemical CO2 Capture and Concentration A. M. Zito, D. Bím, S. Vargas, A. N. Alexandrova, and J. Y. Yang ACS Sustainable Chemistry & Engineering 2022 10 (34), 11387-11395. 10.1021/acssuschemeng.2c03463

Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density. S. Vargas*, M. Hannefarth, Z. Liu, A.N. Alexandrova. Journal of Chemical Theory and Computation 2021 17 (10), 6203-6213. 10.1021/acs.jctc.1c00623.

Team-based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions. (2019). S. Vargas*, S. Zamirpour, S. Menon, A. Rothman, S. Sim, T. Menke, and A. Aspuru-Guzik. Journal of Chemical Education 2020 97 (3), 689-694. 10.1021/acs.jchemed.9b00603.

Seasonal changes in diet and toxicity in the Climbing Mantella frog (Mantella laevigata). N. A. Moskowitz, …, S. Vargas, …, 2018. PLoS ONE 13(12): e0207940. 10.1371/journal.pone.0207940.

Awards

Darleane C. Hoffman Distinguished Postdoctoral Fellowship
UCLA Charles J. Pederson Dissertation Award
Fulbright Research Fellowship
Cum Laude in Chemistry and Physics
Harvard College Research Program Fellowship
UCLA Competitive Edge Fellowship
Ford Foundation Predoctoral Fellowship, Honorable Mention