Defect Diffusion Graph Neural Networks for Materials Discovery in High-Temperature Energy Applications

Journal Article · Tue Aug 26 04:00:00 EDT 2025 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.5c00021· OSTI ID:2589401

Way, Lauren ^[1]; Spataru, Catalin ^[1]; Jones, Reese ^[1]; Trinkle, Dallas ^[2]; Rowberg, Andrew ^[3]; Varley, Joel ^[3]; Wexler, Robert ^[4]; Smyth, Christopher ^[1]; Douglas, Tyra ^[1]; Bishop, Sean ^[1]; Fuller, Elliot ^[1]; McDaniel, Anthony ^[1]; Lany, Stephan ^[5]; Witman, Matthew ^[1]

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1. Sandia National Laboratories
2. University of Illinois at Urbana Champaign
3. Lawrence Livermore National Laboratory
4. Washington University in St. Louis
5. National Renewable Energy Lab., Golden, CO (United States)

The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Hydrogen Fuel Cell Technologies Office (HFTO)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2589401
• Report Number(s): NREL/JA-5K00-91180
• Journal Information: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 17 Vol. 37
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
97 MATHEMATICS AND COMPUTING
defects
diffusion
machine learning
solar thermochemical hydrogen