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Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

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Publicado en:NPJ Computational Materials vol. 10, no. 1 (2024), p. 175
Autor principal: Pathrudkar, Shashank
Otros Autores: Thiagarajan, Ponkrshnan, Agarwal, Shivang, Banerjee, Amartya S., Ghosh, Susanta
Publicado:
Nature Publishing Group
Materias:
Bayesian analysis
Neural networks
Predictions
Electron density
Computer applications
Machine learning
Density functional theory
Thermalization (energy absorption)
Electronic structure
Uncertainty
Mathematical models
Transfer learning
Configurations
Simulation
Electrons
Acceso en línea:Citation/Abstract
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Resumen:The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.
ISSN:2057-3960
DOI:10.1038/s41524-024-01305-7
Fuente:Health & Medical Collection