April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)
Symposium Supporters
2024 MRS Spring Meeting
MT01.11.05

Benchmarking Anharmonicity in Machine Learned Interatomic Potentials

When and Where

Apr 26, 2024
2:45pm - 3:00pm
Room 320, Level 3, Summit

Presenter(s)

Co-Author(s)

Sasaank Bandi1,Chao Jiang2,Chris Marianetti1

Columbia University1,Idaho National Laboratory2

Abstract

Sasaank Bandi1,Chao Jiang2,Chris Marianetti1

Columbia University1,Idaho National Laboratory2
Machine learning (ML) approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, ML interatomic potentials have been shown to predict ground state properties with near density functional theory (DFT) accuracy at a cost similar to conventional interatomic potential approaches. While ML potentials have been extensively tested across various classes of materials and molecules, there is no clear understanding of how well the anharmonicity of any given system is encoded. Here, we benchmark popular ML interatomic potentials using third and fourth order phonon interactions in flourite crystals. An anharmonic hamiltonian was constructed from DFT using our highly accurate and efficient irreducible derivative methods, which was then used to train three classes of ML potentials: Gaussian Approximation Potentials, Behler-Parrinello Neural Networks, and Graph Neural Networks. We evaluate their accuracy in not only reproducing anharmonic interaction terms but also in observables such as phonon linewidths and lineshifts. We then present the results of the models trained on a DFT dataset, showing good and reasonable agreement with the DFT computed third and fourth order interactions, respectively. Finally, we discuss strategies to leverage anharmonic terms in the training procedure to improve the accuracy of ML interatomic potentials.

Symposium Organizers

Raymundo Arroyave, Texas A&M Univ
Elif Ertekin, University of Illinois at Urbana-Champaign
Rodrigo Freitas, Massachusetts Institute of Technology
Aditi Krishnapriyan, UC Berkeley

Session Chairs

Rodrigo Freitas
Rebecca Lindsey

In this Session