将非共价相互作用纳入转移学习的高斯过程回归模型,用于分子模拟
Matthew L Brown1, Bienfait K Isamura1, Jonathan M Skelton1
1Department of Chemistry, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.
Journal of chemical theory and computation
|July 9, 2024
概括
现在的FFLUX模拟包括两体效应,例如通过对二聚体而不是单聚体进行训练来进行电荷转移. 这使得分子动力学更接近量子力学准确度,而不需要列纳德-斯潜力.
科学领域:
- 计算化学的计算化学
- 分子动力学分子动力学
- 机器学习 机器学习
背景情况:
- FFLUX使用机器学习来实现快速分子动力学.
- 目前在单体上训练的模型错过了诸如电荷转移之类的多体效应.
- 列纳德-斯电位用于分散和排斥,需要广泛的参数化.
研究的目的:
- 通过结合两体效果来改进FFLUX模拟.
- 开发和对FFLUX进行基准测试的甲胺二聚物模型.
- 为了使FFLUX模拟能够更好地接近量子力学.
主要方法:
- 训练过的FFLUX模型使用的是formamide二聚物而不是单聚物.
- 利用超参数传输来高效训练高维模型.
- 与量子力学计算对比二进制模型.
主要成果:
- 二元模型使FFLUX模拟能够包括两体效应,如分子间偏振和电荷透.
- 新的方法消除了对莱纳德-斯潜力的需求.
- 超参数转移将训练时间缩短了数量级.
结论:
- 在集群 (二极管) 上训练FFLUX模型显著提高了模拟准确性.
- 这项工作代表了朝着分子动力学量子力学准确性的关键一步.
- 开发的方法允许更准确的模拟分子间相互作用.
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