Pretrained universal machine-learning interatomic potentials(MLIPs)have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods.Fine-tuning...Pretrained universal machine-learning interatomic potentials(MLIPs)have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods.Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient.However,this approach often induces catastrophic forgetting,undermining the generalizability that is a key advantage of pretrained MLIPs.Herein,we propose reEWC,an advanced fine-tuning strategy that integrates Experience Replay and Elastic Weight Consolidation(EWC)to effectively balance forgetting prevention with fine-tuning efficiency.Using Li_(6)PS_(5)Cl(LPSC),a sulfide-based Li solid-state electrolyte,as a fine-tuning target,we show that reEWC significantly improves the accuracy of a pretrained MLIP,resolving well-known issues of potential energy surface softening and overestimated Li diffusivities.Moreover,reEWC preserves the generalizability of the pretrained MLIP and enables knowledge transfer to chemically distinct systems,including other sulfide,oxide,nitride,and halide electrolytes.Compared to Experience Replay and EWC used individually,reEWC delivers clear synergistic benefits,mitigating their respective limitations while maintaining computational efficiency.These results establish reEWC as a robust and effective solution for continual learning in MLIPs,enabling universal models that can advance materials research through large-scale,high-throughput simulations across diverse chemistries.展开更多
基金supported by the Nano & Material Technology Development Programs through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (No. RS-2024-00407995 and No. RS-2024-00450102). The computations were carried out at Korea Institute of Science and Technology Information (KISTI) National Supercomputing Center (KSC-2025-CRE-0110) and at the Center for Advanced Computations (CAC) at Korea Institute for Advanced Study (KIAS).
文摘Pretrained universal machine-learning interatomic potentials(MLIPs)have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio methods.Fine-tuning pretrained MLIPs offers a practical approach to improving accuracy for materials and properties where predictive performance is insufficient.However,this approach often induces catastrophic forgetting,undermining the generalizability that is a key advantage of pretrained MLIPs.Herein,we propose reEWC,an advanced fine-tuning strategy that integrates Experience Replay and Elastic Weight Consolidation(EWC)to effectively balance forgetting prevention with fine-tuning efficiency.Using Li_(6)PS_(5)Cl(LPSC),a sulfide-based Li solid-state electrolyte,as a fine-tuning target,we show that reEWC significantly improves the accuracy of a pretrained MLIP,resolving well-known issues of potential energy surface softening and overestimated Li diffusivities.Moreover,reEWC preserves the generalizability of the pretrained MLIP and enables knowledge transfer to chemically distinct systems,including other sulfide,oxide,nitride,and halide electrolytes.Compared to Experience Replay and EWC used individually,reEWC delivers clear synergistic benefits,mitigating their respective limitations while maintaining computational efficiency.These results establish reEWC as a robust and effective solution for continual learning in MLIPs,enabling universal models that can advance materials research through large-scale,high-throughput simulations across diverse chemistries.
