
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Artificial Intelligence Applied to Battery Research: Hype or Reality?

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.
- Institut Universitaire de France France
- Technical University of Denmark Denmark
- University of Paris France
- Université de Nantes France
- New Sorbonne University France
current hot topic, Science Policy, [SPI] Engineering Sciences [physics], Immunology, Information Systems not elsewhere classified, different battery r, general interest, next generation, Machine Learning, [SPI]Engineering Sciences [physics], battery community, Artificial Intelligence, battery research, yet easily understandable, 401, artificial intelligence, batteries , machine learning, aspects covered, artificial intelligence applied, methods applied, Biological Sciences not elsewhere classified
current hot topic, Science Policy, [SPI] Engineering Sciences [physics], Immunology, Information Systems not elsewhere classified, different battery r, general interest, next generation, Machine Learning, [SPI]Engineering Sciences [physics], battery community, Artificial Intelligence, battery research, yet easily understandable, 401, artificial intelligence, batteries , machine learning, aspects covered, artificial intelligence applied, methods applied, Biological Sciences not elsewhere classified
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).282 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 0.1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
