NRC-EME, 4250 Wesbrook Mall, Vancouver, BC, V6T 1W5, Canada.
Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
Chemphyschem. 2019 Nov 19;20(22):2946-2955. doi: 10.1002/cphc.201900570. Epub 2019 Nov 5.
Similar to advancements gained from big data in genomics, security, internet of things, and e-commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self-learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer-aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor-made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI-driven modeling and computational approaches that are currently applied to those objects. An application-driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO conversion as a use case.
类似于在基因组学、安全、物联网和电子商务中从大数据中获得的进展，通过简化数据源、自主材料合成、快速表征、大数据分析和自学习算法的进展，材料工作流程可以变得更加高效和多产。在电化学材料科学中，数据集很大，非结构化/异构，并且难以从单个数据通道或平台进行处理和分析。计算机辅助材料设计以及数据挖掘、机器学习和预测分析的进步有望为定制功能优化的能源材料提供廉价且加速的途径。电化学能源材料领域的基础研究主要集中在复杂的界面现象和动力学电催化过程上。本文批判性地评估了当前应用于这些对象的人工智能驱动建模和计算方法。引入了一个面向应用的材料智能平台，并考虑到 CO 转化电催化剂材料的开发，作为用例，对其功能进行了详细审查。
