在原子尺度机器学习中解释远程物理学
Incorporating long-range physics in atomic-scale machine learning
摘要：
最成功和最流行的原子尺度属性的机器学习模型从局部性分析中获得了它们的可转移性。大分子或大块材料的性质被写为取决于有限原子中心环境中的构型的贡献的总和。这种方法的明显缺点是它无法捕获非局部、非加性效应，例如由于远程静电或量子干涉而产生的效应。我们通过引入系统的非局部表示来解决这个问题，这些表示被重新映射为局部定义的特征向量，并且在O（3）中是等变的。我们特别考虑一种与静电势具有相同渐近行为的形式。我们证明，这个框架可以捕获非本地，远程物理随机分布的点电荷的静电能，带电的有机分子二聚体的非松弛结合曲线，和液态水的电子介电响应建立一个模型。通过将对长程相关性敏感的系统的表示与以原子为中心的加性模型的可转移性相结合，该方法优于当前最先进的机器学习方案，并提供了一个概念框架，将非局部物理学纳入原子机器学习。
英文摘要：
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture non-local, non-additive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing non-local representations of the system that are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider in particular one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture non-local, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes, and provides a conceptual framework to incorporate non-local physics into atomistic machine learning.
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