npj Computational Materials: 胡建军打造新材料发现的百宝工具箱：MaterialAtlas.org
几百年来，人们一直是通过反复试验或者靠运气和偶然发现来得到新材料。随着计算与信息技术的发展，利用计算模型通过大规模计算仿真发现新材料成为了可能。通过开发基于深度学习神经网络等机器学习算法来学习材料数据库中的已知材料的成分与结构的规律，科学家正在使用人工智能来预测哪些元素组合能合成新材料以及这些预测材料的结构与各种性质。目前，材料学领域使用最广泛的信息平台包括Materials Project（MP）、Aflow-lib和OQMD，它们都主要作为数据源使用。尽管这些主要的数据库都带有一些相关的分析工具，但在新材料发现研究中，还缺少便捷高效的互联网应用程序与工具，用于提高新材料发现的生命周期中材料属性表征、材料性能预测、材料合成、材料的理论发现和设计的研究效率。
该文近期发表于npj Computational Materials (2022) 8: 65，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
The availability and easy access of large-scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, the lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein we first survey current materials informatics web apps and then propose and develop MaterialsAtlas.org, a web-based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including material’s composition and structure validity check (e.g. charge neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, and thermal conductivity), search for hypothetical materials, and utility tools. These user-friendly tools can be freely accessed at http://www.materialsatlas.org. We argue that such materials informatics apps should be widely developed by the community to speed up materials discovery processes.
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