..delta..-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features
Journal Article
·
· ACS Materials Letters
- University of South Carolina
- University of Colorado Boulder
- National Laboratory of the Rockies, Golden, CO (United States)
- Rensselaer Polytechnic Institute
- University of Utah
In this work, we present a ..delta..-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (..epsilon..nkHSE) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and k-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ..epsilon..nkHSE.
- Research Organization:
- National Laboratory of the Rockies (NLR), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 3015922
- Report Number(s):
- NLR/JA-2C00-99062
- Journal Information:
- ACS Materials Letters, Journal Name: ACS Materials Letters Journal Issue: 12 Vol. 7
- Country of Publication:
- United States
- Language:
- English
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