神经网络对分子间相互作用的纠正 分子力场的条件 捕获 核量子效应 液体热力学性能计算中的核量子效应
Igor V Kurnikov1, Leonid Pereyaslavets1, Ganesh Kamath1
1InterX Inc., (a Subsidiary of NeoTX Therapeutics Ltd.), 805 Allston Way, Berkeley, California 94710, United States.
Journal of chemical theory and computation
|January 19, 2024
概括
我们开发了一种使用神经网络准确模拟凝聚物质中的核量子效应 (NQE) 的新方法,使复杂的模拟更快,更有效地用于化学和生物系统.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
背景情况:
- 核量子效应 (NQE) 对于准确描述凝聚物质系统至关重要.
- 传统的方法,如通路积分分子动力学 (PIMD),在计算上昂贵.
- 开发包括NQE在内的高效方法对于推进模拟至关重要.
研究的目的:
- 引入一种用于将NQE纳入冷凝物质模拟的新方法.
- 开发一种计算效率高的方法,避免显式PIMD模拟.
- 通过计算NQE,准确预测化学和生物系统的特性.
主要方法:
- 纳入短距离神经网络 (NN) 校正到ARROW ab initio配合的分子力场中.
- 采用分子二次体的PIMD模拟的平均相互作用能量和力进行了NN校正.
- 将NN校正的力场应用于液态水和甲模拟.
主要成果:
- 经NN校正的力场成功地复制了液态水和甲的NQE.
- 实现了包括密度,辐射分布函数 (RDF),蒸发热 (HVAP) 和溶解自由能量在内的属性的准确预测.
- 该方法证明了适合复杂系统的准确性和局部性.
结论:
- 开发的基于NN的力场校正提供了一种有效的方式,可以将NQE纳入模拟.
- 这种方法绕过了对计算密集型PIMD模拟的需求.
- 该方法显示了在蛋白质等复杂的异质系统中应用的潜力.
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