Machine learning of accurate energy-conserving molecular force fields.
Machine learning of accurate energy-conserving molecular force fields.
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DOI：
10.1126/sciadv.1603015
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发表时间：
2017-05
期刊：
影响因子：
13.6
通讯作者：
Müller KR
中科院分区：
文献类型：
--
作者：
Chmiela S;Tkatchenko A;Sauceda HE;Poltavsky I;Schütt KT;Müller KR
能量守恒定律被用于开发一种高效的机器学习方法来构建精确的力场。
利用能量守恒——经典和量子力学封闭系统的一个基本属性——我们开发了一种高效的梯度域机器学习（GDML）方法，使用从头算分子动力学（AIMD）轨迹中有限数量的样本构建精确的分子力场。GDML的实现能够重现中等大小分子的全局势能面，对于能量的精度为0.3千卡/摩尔，对于原子力的精度为1千卡/摩尔·埃⁻¹，且仅使用1000个构象几何结构进行训练。我们针对包括苯、甲苯、萘、乙醇、尿嘧啶和阿司匹林等分子的AIMD轨迹证明了这一精度。在我们的工作中，通过在遵循能量守恒定律的向量值函数的希尔伯特空间中进行学习，完成了构建保守力场的挑战。GDML方法能够以显式AIMD计算成本的一小部分对分子进行定量的分子动力学模拟，从而允许构建具有高级从头算方法的准确性和可转移性的高效力场。
The law of energy conservation is used to develop an efficient machine learning approach to construct accurate force fields. Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.