Abstract:

In many problems spanning transition-metal catalysis and quantum materials, quantitative prediction hinges on treating electron correlation and electron–phonon coupling beyond standard mean-field and perturbative approaches. In this talk, I will describe our efforts to advance the auxiliary field quantum Monte Carlo (AFQMC) method, enabling first-principles electronic structure calculations in challenging correlated systems with accuracy beyond state of the art coupled cluster methods and more favorable cost scaling. I will also discuss how neural quantum states can be used to model electron–phonon dynamics and compute spectroscopic observables. Finally, I will present results on possible microscopic mechanisms underlying chirality induced spin selectivity (CISS).

Bio:

Ankit Mahajan is a postdoctoral researcher at Columbia University working with Prof. David R. Reichman. He earned his PhD in Chemical Physics from the University of Colorado Boulder (advisor: Prof. Sandeep Sharma) and his undergraduate degree in Physics from IIT Bombay. His research develops and applies ab initio electronic structure and dynamics methods for strongly correlated systems, combining ideas from quantum chemistry, condensed matter physics, and machine learning.