Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T50: Learning Materials Properties and Dynamics with Graph Neural Network Models
11:30 AM–1:18 PM,
Thursday, March 9, 2023
Room: Room 320
Sponsoring
Unit:
DCOMP
Chair: Boris Kozinsky, Harvard University
Abstract: T50.00002 : Graph Neural Networks for Molecules and Materials
12:06 PM–12:42 PM
Presenter:
Johannes Gasteiger
(Technical University Munich)
Author:
Johannes Gasteiger
(Technical University Munich)
In the first part of this talk, I will introduce the framework of message passing neural networks (MPNNs) and present ways of incorporating directional and geometric information in this framework. To alleviate the short-range nature of the MPNN framework, I will furthermore present a method to learn long-range interactions inspired by Ewald summation.
In the second part of this talk, I will dive into the evaluation of GNNs for energy and force prediction. I will first discuss evaluating GNNs for molecular dynamics simulations, highlighting possible pitfalls in this task. Finally, I will analyze the differences between models developed for small molecules and those optimized for large and diverse atomic systems such as the open catalyst (OC20) dataset.
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