Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session Q60: Machine Learning of Molecules and Materials: Electronic Structure II
3:00 PM–5:48 PM,
Wednesday, March 6, 2024
Room: 207AB
Sponsoring
Unit:
DCOMP
Chair: Stefano Falletta, Harvard University
Abstract: Q60.00009 : Predicting Quantum Monte Carlo Charge Densities using Graph Neural Networks*
5:00 PM–5:12 PM
Presenter:
Panchapakesan Ganesh
(Oak Ridge National Lab)
Authors:
Panchapakesan Ganesh
(Oak Ridge National Lab)
Abdulgani Annaberdiyev
(Oak Ridge National Lab)
Fan Shu
(Georgia Institute of Technology)
Victor Fung
(Georgia Institute of Technology)
quantum materials. Although density functional approximations have been improving in energetics,
the progress in charge densities has been stagnant. We use quantum Monte Carlo (QMC) to calculate
charge densities as well as total and kinetic energies of over 1700 QM9 dataset molecules with
closed-shell singlet and open-shell triplet configurations. A message-passing graph neural network
designed for grid-based data was trained to predict the charge densities. We explore the viability
of reaching accurate predictions using a small QMC dataset, which we make publicly available for
future machine-learning models.
*This work has been supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences, and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials. Some part of the research conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.
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