通过持续学习,有效地训练神经网络的化学和酶反应潜力
Yao-Kun Lei1,2,3, Kiyoshi Yagi1,2,4, Yuji Sugita1,2,3,5
1Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan.
Journal of chemical theory and computation
|March 11, 2025
概括
对于更广泛的应用,机器学习力场得到了改进. 结合可转移性和持续学习策略,可以为各种化学环境高效地构建力场.
科学领域:
- 计算化学计算化学
- 机器学习在化学中的应用
背景情况:
- 机器学习 (ML) 方法为分子建模提供了传统电子结构理论的高效替代方案.
- 目前的ML力场因有限的训练数据和专注于封闭系统而难以推断和可转移.
- 现有的模型在复杂的凝聚相环境中表现不佳,例如酶反应,导致高计算成本.
研究的目的:
- 提高ML力场的可转移性和适用性,用于复杂,异质的环境,包括酶反应.
- 开发一个高效和自主训练策略,用于ML力场使用持续学习.
- 在各种化学反应介质中创建适用于各种化学反应介质的多功能力场.
主要方法:
- 开发了一个机器学习/分子力学 (ML/MM) 模型,利用电静运算符的泰勒扩展.
- 将ML/MM模型的可转移性扩展到复杂的环境,特别是酶反应.
- 实施了使用记忆数据集的持续学习策略,以便在飞行中使用新数据进行训练.
主要成果:
- ML/MM模型在简单的溶剂系统中显示出高的可转移性.
- 扩展模型在更复杂的异质环境中显示出有希望的可转移性,包括酶反应.
- 可转移性和持续学习的结合方法为各种应用程序提供了高效的力场构造.
结论:
- 整合ML / MM模型与泰勒扩展和持续学习的整合显著提高了力场的可转移性和适用性.
- 这一战略使得能够开发出强大的力场,能够处理各种环境介质中的化学反应.
- 这些发现为更一般,更高效的计算化学模拟铺平了道路.
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