April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)

Event Supporters

2024 MRS Spring Meeting
MT01.02.04

Materials Discovery for High-Temperature Clean-Energy Applications (and Beyond) via Defect Graph Neural Networks

When and Where

Apr 22, 2024
2:45pm - 3:00pm
Room 320, Level 3, Summit

Presenter(s)

Co-Author(s)

Matthew Witman1,Anuj Goyal2,Tadashi Ogitsu3,Anthony McDaniel1,Stephan Lany2

Sandia National Laboratories1,National Renewable Energy Laboratory2,Lawrence Livermore National Laboratory3

Abstract

Matthew Witman1,Anuj Goyal2,Tadashi Ogitsu3,Anthony McDaniel1,Stephan Lany2

Sandia National Laboratories1,National Renewable Energy Laboratory2,Lawrence Livermore National Laboratory3
We present a graph neural network (GNN) approach that fully automates the prediction of defect formation enthalpies for any crystallographic site from the ideal crystal structure, without the need to create defected atomic structure models as input [1]. Using density functional theory (DFT) reference data for vacancy defects in oxides, we trained a defect GNN (dGNN) model that replaces the DFT supercell relaxations otherwise required for each symmetrically unique crystal site. Interfaced with thermodynamic calculations of reduction entropies and associated free energies, the dGNN model is applied to the screening of oxides in the Materials Project database, connecting the zero-Kelvin defect enthalpies to high-temperature process conditions relevant for solar thermochemical hydrogen production and other energy applications. The dGNN approach is applicable to arbitrary structures with an accuracy limited principally by the amount and diversity of the training data, and it is generalizable to other defect types and advanced graph convolution architectures. It will aid tackling future materials discovery problems in clean energy and beyond.<br/><br/>[1] Witman, M. et al. <i>Nature Computational Science</i>, <b>3</b>, 675–686 (2023)

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

Raymundo Arroyave
Felipe H. da Jornada

In this Session