AI and machine learning in the design and synthesis of crystalline functional materials
- Submission status
- Open
- Submission deadline
Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health science. However, traditional ways of discovering and characterizing materials are time and resource consuming due to the complexity of chemical compositions, structures and targeted properties. The integration of AI tools such as machine learning algorithms with physics-based modelling and experimental automation is accelerating the computational design of new functional materials as well as their experimental synthesis.
This collection aims to highlight recent advances and applications of AI and machine learning methods in solid-state materials science with a focus on crystalline systems. The collection consists of three sections.
The first section concerns applications of AI and machine learning methods to accelerate the discovery of new crystalline materials, either through the high-throughput computational search of stable or metastable compounds, or the automation of experimental synthesis attempts. The second section focuses on the usage of these methods to accelerate material property prediction. The third section is dedicated to methodological developments and the proposals of new protocols.
This Collection is open for submissions from all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of Nature Communications, Communications Physics and Scientific Reports more generally.
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This Collection has not been supported by sponsorship.
