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受材料基因组计划、算法发展和数据驱动的研究在其他领域取得巨大成功的推动，材料科学研究中的信息学方法已逐渐成形。该方法采用机器学习模型，仅依赖已有的数据便可快速做出预测，既不需通过直接的实验，也不需要求解基本方程来计算/模拟。该方法对于难以用传统方法测量或计算的材料性能研究将会十分有效，给材料信息学添加了机器学习的翅膀。来自美国康涅狄格州立大学材料科学与工程系及材料科学研究所的Rampi Ramprasad教授，综述了过去十年来基于数据驱动的“材料信息学”的成功策略，特别强调了材料“指纹”（也称作“描述符”，可有多种类型和多个尺度）的选择。综述还指出了该领域所面临的一些挑战以及近期要克服的困难。该文近期发表于npj Computational Materials 3:54 (2017); doi:10.1038/s41524-017-0056-5。

英文标题与摘要如下，点击阅读原文可以自由获取论文PDF。
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, Arun Mannodi-Kanakkithodi & Chiho Kim
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.
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