论文标题
具有梯度域机器学习（GDML）的分子力场：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
论文作者
论文摘要
现代的机器学习力场（ML-FF）能够以高级$ ab〜Initio $方法的准确性来产生能量和力预测，但计算成本要低得多。另一方面，经典的分子力学力场（MM-FF）采用固定的功能形式，并且往往较少准确，但在同一类的分子之间更快且可转移。在这项工作中，我们研究了两种方法如何相互补充。我们将ML-FF的能力与MM-FFS重建动态和热力学可观察物的能力进行了对比，以便对两种方法之间的差异有定性的理解。该分析使我们能够通过重新应对短程和更富有表现力的术语来修改广义琥珀色场（GAFF），以使其更准确，而不会牺牲使MM-FFS如此成功的关键特性。
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.
