嵌入式物理学机器学习，用于粗粒度和集体变量发现，无需数据
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
摘要：
我们提出了一种新的学习框架，该框架始终嵌入底层物理学，同时绕过了分子动力学（MD）背景下大多数现代数据驱动粗粒度方法的一个显著缺点，即，大数据的可用性。足够大的训练数据集的生成提出了一个计算上要求很高的任务，而原子配置空间的完全覆盖是不能保证的。因此，数据驱动的粗粒度模型的探索能力是有限的，可能会产生有偏见的“预测”工具。我们提出了一个新的目标的基础上反向Kullback-Leibler分歧，充分结合了现有的物理形式的原子力场。而不是从数据生成过程中分离模型学习-后者依赖于模拟由力场控制的原子运动-我们在预测粗粒度模型提出的样本配置中查询原子力场。因此，学习依赖于力场的评估，但不需要任何MD模拟。生成的粗粒度模型作为一个有效的代理模型预测原子的配置和估计相关的可观测量。除了获得一个预测粗粒度模型，我们证明，在发现的低维表示，集体变量（CV）相关的物理化学性质，这是必不可少的了解未开发的复杂系统。我们展示了算法的预测能力和物理意义的双峰势能函数和丙氨酸二肽的CV显示方面的进步。
英文摘要：
We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in the context of molecular dynamics (MD), i.e., the availability of big data. The generation of a sufficiently large training dataset poses a computationally demanding task, while complete coverage of the atomistic configuration space is not guaranteed. As a result, the explorative capabilities of data-driven coarse-grained models are limited and may yield biased "predictive" tools. We propose a novel objective based on reverse Kullback-Leibler divergence that fully incorporates the available physics in the form of the atomistic force field. Rather than separating model learning from the data-generation procedure - the latter relies on simulating atomistic motions governed by force fields - we query the atomistic force field at sample configurations proposed by the predictive coarse-grained model. Thus, learning relies on the evaluation of the force field but does not require any MD simulation. The resulting generative coarse-grained model serves as an efficient surrogate model for predicting atomistic configurations and estimating relevant observables. Beyond obtaining a predictive coarse-grained model, we demonstrate that in the discovered lower-dimensional representation, the collective variables (CVs) are related to physicochemical properties, which are essential for gaining understanding of unexplored complex systems. We demonstrate the algorithmic advances in terms of predictive ability and the physical meaning of the revealed CVs for a bimodal potential energy function and the alanine dipeptide.
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