电池(电)
计算机科学
数据科学
人工智能
化学
工程伦理学
管理科学
工程类
量子力学
物理
功率(物理)
作者
Teo Lombardo,Marc Duquesnoy,Hassna El-Bouysidy,Fabian Årén,A. Gallo‐Bueno,Peter Bjørn Jørgensen,Arghya Bhowmik,Arnaud Demortière,Elixabete Ayerbe,Francisco Alcaide,Marine Reynaud,Javier Carrasco,Alexis Grimaud,Chao Zhang,Tejs Vegge,Patrik Johansson,Alejandro A. Franco
出处
期刊:Chemical Reviews
[American Chemical Society]
日期:2021-09-16
卷期号:122 (12): 10899-10969
被引量:284
标识
DOI:10.1021/acs.chemrev.1c00108
摘要
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
科研通智能强力驱动
Strongly Powered by AbleSci AI