機械学習でインスタントン経路の計算精度が向上（Machine Learning Acceleration of Instanton Pathways）

2025-09-09 パシフィック・ノースウェスト国立研究所（PNNL）

＜関連情報＞

• https://www.pnnl.gov/publications/machine-learning-acceleration-instanton-pathways
• https://pubs.acs.org/doi/10.1021/acs.jctc.5c00673

線積分誘導弾性バンド法とガウス過程回帰を用いたインスタントン理論の加速 Accelerating Instanton Theory with the Line Integral Nudged Elastic Band Method and Gaussian Process Regression

Chenghao Zhang,Amke Nimmrich,Britta A. Johnson,Gregory K. Schenter,Munira Khalil,and Niranjan Govind
Journal of Chemical Theory and Computation  Published: July 17, 2025
DOI:https://doi.org/10.1021/acs.jctc.5c00673

Abstract

Quantum tunneling plays a fundamental role in many chemical reactions, particularly proton transfer processes. Ring polymer instanton theory offers a practical framework for computing tunneling rates in complex molecular systems. However, applying the ring polymer instanton method with a potential energy surface generated on-the-fly using electronic structure calculations can be computationally demanding. In this work, we present a new efficient implementation of the ring polymer instanton method by combining the Line Integral Nudged Elastic Band (LI-NEB) approach with Gaussian Process Regression (GPR). We benchmarked this method on prototypical ground-state proton transfer systems, including the benchmark gas-phase hydrogen abstraction reaction H + CH₄ → H₂ + CH₃, malonaldehyde, and Z-3-amino-propenal (aminopropenal). Our results show that this approach is an order of magnitude faster than traditional instanton algorithms while maintaining excellent agreement with their tunneling rates. This development opens the door to studying proton transfer in larger systems with improved efficiency.