PREC Track 1: Cal State LA - MolSSI PREC Pathway to Diversity Program

California State L A University Auxiliary Services Inc., Los Angeles CA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The Cal State LA-MolSSI PREC (Partnership for Research and Education in Chemistry) Pathway to Diversity Program is a collaboration between California State University, Los Angeles, a comprehensive public university and Hispanic Serving Institution, and the Molecular Software Sciences Institute (MolSSI) at Virginia Tech to incorporate machine learning (ML) techniques in molecular simulation research and develop innovative pedagogical materials to train early-stage undergraduate students in computational science. Cal State LA undergraduate and master’s students will participate year-round in mentored research and attend an annual workshop at MolSSI. Community college students will take part in mentored summer research experiences at Cal State LA alongside these students. Additionally, early-stage undergraduate students from local community colleges and Cal State LA will participate in an annual computational workshop taught by instructors from Cal State LA and MolSSI that emphasizes scientific programming and a variety of molecular simulation and ML techniques, as well as professional development activities. Overall, this PREC aims to make a significant contribution to the recruitment and training of the next generation of molecular simulation scientists who will require a deep understanding of both physical and chemical principles and computational techniques. Machine learning (ML) methods have transformed the fields of chemistry and molecular sciences in recent years, and will continue to do so in the future. The Cal State LA-MolSSI PREC (Partnership for Research and Education in Chemistry) will be organized around three thematic research thrusts that each use ML and physics-based simulation methods to create new computational models applicable to a range of chemical and biochemical phenomena. Thrust 1 will focus on developing ML approaches for computing the relative entropies and thermodynamic stabilities of molecular crystal polymorphs. Thrust 2 will aim to develop a hybrid physics-based and ML approach for predicting the relative binding free energies of small protein-ligand complexes. Thrust 3 will use ML to parametrize small molecule force fields that include a direct polarization electrostatic model and other advanced nonbonded potentials. The results of this research will help answer pressing questions in chemistry, biophysics, materials science, and pharmacology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.