从第一性原理学习一致的分子力学力场
Learning consistent molecular mechanics force fields from first principles
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中文总结 AI 辅助
提出统一方法 grappa-fullFF，从第一性原理同时一致地学习键合与非键合参数，通过物理正则化与电荷平衡架构提升精度，在几何优化和构象采样上达到先进水平。
中文摘要 AI 辅助
经典力场（FFs）即使机器学习原子间势（MLIPs）接近从头算精度，仍然是大规模模拟的主力工具。它们将总构型能分解为简单的有效相互作用，其参数传统上基于原子或键类型分配，从而实现高效模拟，但也限制了它们跨构型的适应能力。最近的机器学习方法通过将键合参数推断为局部原子环境的函数，提高了这些力场中键合参数的准确性和可迁移性，但仍依赖经验非键合参数进行实际模拟。在这项工作中，我们引入了一种统一方法，\texttt{grappa-fullFF}，它从从头算参考数据中一致且同时地学习键合和非键合参数。通过结合物理启发的正则化（通过静电势的监督）和促进电荷平衡的架构，我们的模型恢复了准确的电响应性质，在几何优化基准上达到了最先进的准确性，并再现了经典和现有机器学习力场的构象采样，而无需依赖外部分配的非键合参数。
英文摘要
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose total configuration energies into simple effective interactions whose parameters are traditionally assigned based on atom or bond types, enabling efficient simulations but also limiting their ability to adapt across configurations. Recent machine learning approaches have improved the accuracy and transferability of bonded parameters in these FFs by inferring them as functions of local atomic environments, but still rely on empirical nonbonded parameters for practical simulations. In this work, we introduce a unified approach, \texttt{grappa-fullFF}, which learns both bonded and nonbonded parameters \emph{consistently} and simultaneously from ab initio reference data. By incorporating physically inspired regularization via supervision of the electrostatic potential and an architecture that facilitates charge equilibration, our model recovers accurate electric response properties, achieves state-of-the-art accuracy on geometry optimization benchmarks, and reproduces the conformational sampling of both classical and existing machine-learned FFs, without relying on externally assigned nonbonded parameters.
发表机构
- Max Planck Institute for Polymer Research（马克斯·普朗克聚合物研究所）
- Heidelberg University（海德堡大学）
- Heidelberg Institute for Theoretical Studies（海德堡理论研究所）
- Flatiron Institute（熨斗研究所）
机构由 AI 辅助整理，请以论文原文为准。
