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。

材料专家 —— 材料发现的人工智能

Materials Expert-Artificial Intelligence for Materials Discovery

Accurate and comprehensive material databases extracted from research papers
are critical for materials science and engineering but require significant
human effort to develop. In this paper we present a simple method of extracting
materials data from full texts of research papers suitable for quickly
developing modest-sized databases. The method requires minimal to no coding,
prior knowledge about the extracted property, or model training, and provides
high recall and almost perfect precision in the resultant database. The method
is fully automated except for one human-assisted step, which typically requires
just a few hours of human labor. The method builds on top of natural language
processing and large general language models but can work with almost any such
model. The language models GPT-3/3.5, bart and DeBERTaV3 are evaluated here for
comparison. We provide a detailed detailed analysis of the methods performance
in extracting bulk modulus data, obtaining up to 90% precision at 96% recall,
depending on the amount of human effort involved. We then demonstrate the
methods broader effectiveness by developing a database of critical cooling
rates for metallic glasses.

本文主要介绍了一种从研究论文全文中提取材料数据以建立数据库的简单方法，该方法基于自然语言处理和大型通用语言模型，无需编程或模型培训等专业知识，可实现高召回率和几乎完美精度。作者还评估了多种语言模型（GPT-3/3.5，bart 和 DeBERTaV3），并在提取体积模量数据方面取得了良好的表现。最后，作者还通过开发金属玻璃的临界冷却速率数据库展示了该方法可以扩展到其他研究领域。

使用通用语言模型从文本中提取材料数据的灵活模型无关方法

Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models

Open material databases storing hundreds of thousands of material structures
and their corresponding properties have become the cornerstone of modern
computational materials science. Yet, the raw outputs of the simulations, such
as the trajectories from molecular dynamics simulations and charge densities
from density functional theory calculations, are generally not shared due to
their huge size. In this work, we describe a cloud-based platform to facilitate
the sharing of raw data and enable the fast post-processing in the cloud to
extract new properties defined by the user. As an initial demonstration, our
database currently includes 6286 molecular dynamics trajectories for amorphous
polymer electrolytes and 5.7 terabytes of data. We create a public analysis
library at this https URL to extract multiple properties
from the raw data, using both expert designed functions and machine learning
models. The analysis is run automatically with computation in the cloud, and
results then populate a database that can be accessed publicly. Our platform
encourages users to contribute both new trajectory data and analysis functions
via public interfaces. Newly analyzed properties will be incorporated into the
database. Finally, we create a front-end user interface at
this https URL for browsing and visualization of our data. We
envision the platform to be a new way of sharing raw data and new insights for
the computational materials science community.

本文介绍了一个基于云平台的系统，旨在通过共享原始数据和云端快速后处理来提取用户定义的新特性，以实现计算材料科学社区的数据共享和新见解。