The advent of material databases provides an unprecedented opportunity to
uncover predictive descriptors for emergent material properties from vast data
space. However, common reliance on high-throughput ab initio data necessarily
inherits limitations of such data: mismatch with experiments. On the other
hand, experimental decisions are often guided by an expert's intuition honed
from experiences that are rarely articulated. We propose using machine learning
to "bottle" such operational intuition into quantifiable descriptors using
expertly curated measurement-based data. We introduce "Materials
Expert-Artificial Intelligence" (ME-AI) to encapsulate and articulate this
human intuition. As a first step towards such a program, we focus on the
topological semimetal (TSM) among square-net materials as the property inspired
by the expert-identified descriptor based on structural information: the
tolerance factor. We start by curating a dataset encompassing 12 primary
features of 879 square-net materials, using experimental data whenever
possible. We then use Dirichlet-based Gaussian process regression using a
specialized kernel to reveal composite descriptors for square-net topological
semimetals. The ME-AI learned descriptors independently reproduce expert
intuition and expand upon it. Specifically, new descriptors point to
hypervalency as a critical chemical feature predicting TSM within square-net
compounds. Our success with a carefully defined problem points to the "machine
bottling human insight" approach as promising for machine learning-aided
material discovery.

通过机器学习将操作性直觉转化为可量化描述符，以及通过结构信息指示的容限因子，研究和发现拓扑半金属等材料。