Pavan Ravindra

Program Year:

University:

Columbia University

Field of Study:

Chemical Physics

Advisor:

David Reichman

Degree(s):

M.Phil. Chemistry, University of Cambridge, 2022; B.S. Biochemistry, and B.S. Computer Science, University of Maryland College Park, 2021

Practicum Experience(s)

Brookhaven National Laboratory (2024)

Practicum Supervisor(s):

Deyu

Practicum Title:

Machine Learning for X-Ray Absorption Spectroscopy

Summary of Research

I use tools from physics and machine learning to study a wide range of chemical systems, including supercooled liquids, nanomaterials, and biomolecules. My current projects include:

1. Developing machine learning models for efficient simulation of dynamical systems
2. Using neural networks to represent quantum states of fermionic systems
3. Applying data-driven methods to connect experimental and simulated X-ray absorption spectra

Annual Program Review Abstracts

A Machine Learning Force Field Study of Hydrogen Bonding in Nanoconfined Water (2024)

Publications

Ravindra, P., Advincula, X., Shi, B., Coles, S., Michaelides, A., & Kapil, V. (2024). Nuclear quantum effects induce superionic proton transport in nanoconfined water. arXiv.

Ravindra, P., Advincula, X., Schran, C., Michaelides, A., Kapil, V. (2024). Quasi-one-dimensional hydrogen bonding in nanoconfined ice. Nature Communications.

Smith, Z., Ravindra, P., Wang, Y., Cooley, R., Tiwary, P. (2020). Discovering Protein Conformational Flexibility Through Artificial Intelligence Aided Molecular Dynamics. The Journal of Physical Chemistry B.

Ravindra, P., Smith, Z., Tiwary, P. (2020). Automatic mutual information noise omission (AMINO): Generating order parameters for molecular systems. Molecular Systems Design & Engineering.

Awards

- Jack Miller Teaching Award
- Churchill Scholarship
- Goldwater Scholarship
- Banneker/Key Scholarship