无监督机器学习从成像数据中发现化学和物理转化途径
Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data
摘要：
我们表明，无监督的机器学习可以用来学习物理和化学转化途径，从观测的微观数据，如原子分辨的图像在扫描透射电子显微镜（STEM）和压电响应力显微镜（PFM）的铁电畴结构所示。为了在STEM中实现这种分析，我们假设原子的存在，原子类的离散性，以及观察到的STEM对比度和原子单位的存在之间存在明确的关系。在PFM中，我们假设了测量信号和偏振分布之间的严格定义的关系。只有这些假设，我们开发了一种利用旋转不变变分自动编码器（rVAE）的机器学习方法，该方法可以识别材料中观察到的现有结构单元。该方法使用少量的潜变量编码图像序列中包含的信息，允许通过系统的潜空间探索化学和物理转化途径。结果表明，高准确性的成像数据可以用来推导基本的物理和化学机制，通过提供编码的观察到的结构，作为结构序参量自下而上的等价物。该方法还证明了变分的潜力（即，贝叶斯）方法的物理科学，并将刺激开发新的方式来编码的编码器-解码器架构中的物理约束，和生成物理定律，拓扑不变性，和因果关系的潜在空间的VAE。
英文摘要：
We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM). To enable this analysis in STEM, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. In PFM, we assumed the uniquely-defined relationship between the measured signal and polarization distribution. With only these postulates, we developed a machine learning method leveraging a rotationally-invariant variational autoencoder (rVAE) that can identify the existing structural units observed within a material. The approach encodes the information contained in image sequences using a small number of latent variables, allowing the exploration of chemical and physical transformation pathways via the latent space of the system. The results suggest that the high-veracity imaging data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods for physical sciences and will stimulate the development of new ways to encode physical constraints in the encoder-decoder architectures, and generative physical laws, topological invariances, and causal relationships in the latent space of VAEs.
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