分子力场与被动域机器学习（GDML）：与经典力场的比较和协同作用
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
摘要：
现代机器学习力场（ML-FF）能够以高级$ab~initio$方法的精度产生能量和力预测，但计算成本要低得多。另一方面，经典分子力学力场（MM-FF）采用固定的功能形式，往往是不太准确，但相当快，并可转移之间的分子相同的类。在这项工作中，我们将研究这两种方法如何相互补充。我们对比的能力ML-FF重建动态和热力学可观的MM-FFs，以获得定性的理解这两种方法之间的差异。这种分析使我们能够修改广义琥珀力场（GAFF），通过重新参数化短程和键合相互作用与更多的表达条款，使他们更准确，而不牺牲的关键属性，使MM-FF如此成功。
英文摘要：
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level $ab~initio$ methods, but at a much lower computational cost. On the other hand, classical molecular mechanics force fields (MM-FF) employ fixed functional forms and tend to be less accurate, but considerably faster and transferable between molecules of the same class. In this work, we investigate how both approaches can complement each other. We contrast the ability of ML-FF for reconstructing dynamic and thermodynamic observables to MM-FFs in order to gain a qualitative understanding of the differences between the two approaches. This analysis enables us to modify the generalized AMBER force field (GAFF) by reparametrizing short-range and bonded interactions with more expressive terms to make them more accurate, without sacrificing the key properties that make MM-FFs so successful.
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