In the field of inorganic materials science, there is a growing demand to
extract knowledge such as physical properties and synthesis processes of
materials by machine-reading a large number of papers. This is because
materials researchers refer to many papers in order to come up with promising
terms of experiments for material synthesis. However, there are only a few
systems that can extract material names and their properties. This study
proposes a large-scale natural language processing (NLP) pipeline for
extracting material names and properties from materials science literature to
enable the search and retrieval of results in materials science. Therefore, we
propose a label definition for extracting material names and properties and
accordingly build a corpus containing 836 annotated paragraphs extracted from
301 papers for training a named entity recognition (NER) model. Experimental
results demonstrate the utility of this NER model; it achieves successful
extraction with a micro-F1 score of 78.1%. To demonstrate the efficacy of our
approach, we present a thorough evaluation on a real-world automatically
annotated corpus by applying our trained NER model to 12,895 materials science
papers. We analyze the trend in materials science by visualizing the outputs of
the NLP pipeline. For example, the country-by-year analysis indicates that in
recent years, the number of papers on "MoS2," a material used in perovskite
solar cells, has been increasing rapidly in China but decreasing in the United
States. Further, according to the conditions-by-year analysis, the processing
temperature of the catalyst material "PEDOT:PSS" is shifting below 200 degree,
and the number of reports with a processing time exceeding 5 h is increasing
slightly.

该研究提出了一种基于自然语言处理的大规模流程，用于从材料科学文献中提取材料名称和特性，以便进行材料科学的搜索和检索。实验证明这种方法是成功的，可以用来分析材料科学的趋势，如 MoS2 等材料在中国不断增加，而在美国减少。

运用自然语言处理技术分析无机材料文献的研究趋势

Analyzing Research Trends in Inorganic Materials Literature Using NLP

Density functional theory offers a very accurate way of computing materials
properties from first principles. However, it is too expensive for modelling
large-scale molecular systems whose properties are, in contrast, computed using
interatomic potentials.
The present paper considers, from a mathematical point of view, the problem
of constructing interatomic potentials that approximate a given
quantum-mechanical interaction model. In particular, a new class of
systematically improvable potentials is proposed, analyzed, and tested on an
existing quantum-mechanical database.

本文提出并分析了一种新的可系统改进的势能模型，以近似给定的量子力学相互作用模型，用于计算大规模分子系统的材料特性。

矩张势：一类系统可改进的原子间势

Moment Tensor Potentials: a class of systematically improvable  interatomic potentials

Statistical learning of materials properties or functions so far starts with
a largely silent, non-challenged step: the choice of the set of descriptive
parameters (termed descriptor). However, when the scientific connection between
the descriptor and the actuating mechanisms is unclear, causality of the
learned descriptor-property relation is uncertain. Thus, trustful prediction of
new promising materials, identification of anomalies, and scientific
advancement are doubtful. We analyse this issue and define requirements for a
suited descriptor. For a classical example, the energy difference of
zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a
meaningful descriptor can be found systematically.

通过分析材料学中的描述符选择问题，定义了一个合适的描述符要求，并以典型半导体结构的能量差为例，演示了如何系统地找到有意义的描述符。