期刊
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
卷 142, 期 48, 页码 20273-20287出版社
AMER CHEMICAL SOC
DOI: 10.1021/jacs.0c09105
关键词
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资金
- European Research Council (ERC) under the European Union [666983]
- NCCR-MARVEL - Swiss National Science Foundation
- PrISMa Project through ACT Programme (Accelerating CCS Technologies, Horizon 2020 Project) [299659, 294766]
- Department for Business, Energy & Industrial Strategy (BEIS)
- NERC Research Council, United Kingdom
- EPSRC Research Council, United Kingdom
- Research Council of Norway (RCN)
- Swiss Federal Office of Energy (SFOE)
- U.S. Department of Energy
- TOTAL
- Equinor
- Swiss National Science Foundation (SNSF) [200021_172759]
- Swiss National Science Foundation (SNF) [200021_172759] Funding Source: Swiss National Science Foundation (SNF)
Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.
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