Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session S01: Machine Learning and Neural Networks in Chemical Physics
8:00 AM–10:48 AM,
Thursday, March 17, 2022
Room: McCormick Place W-175A
Chair: Susan Kempinger, North Central College
Abstract: S01.00007 : Machine learning Kohn-Sham potentials in time-dependent density functional theory
9:12 AM–9:24 AM
James D Whitfield
This work is funded by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under the Quantum Computing Application Teams program.
From the classical Hamilton’s equations, a neural network is trained using the exact time-evolved density. The constructed neural network gives the Kohn-Sham energy functional and with it the exchange-correlation functional. We take the advantage of the differentiable nature of the neural network to compute the necessary Kohn-Sham potential under the adiabatic approximation. We have performed numerical tests on a one-dimensional two-electron system to investigate numerical instabilities in our potential inversion method and explore the consequences of the adiabatic approximation.
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