An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery
| Autor: | Süleyman Er, Murat Cihan Sorkun, Séverin Astruc, J. M. Vianney A. Koelman |
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| Přispěvatelé: | Center for Computational Energy Research, EIRES Chem. for Sustainable Energy Systems, EAISI Foundational |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Science and engineering Interoperability Scientific discovery 02 engineering and technology 010402 general chemistry 01 natural sciences lcsh:TA401-492 General Materials Science Complex problems lcsh:Computer software Virtual screening business.industry Recipe 021001 nanoscience & nanotechnology 0104 chemical sciences Computer Science Applications lcsh:QA76.75-76.765 Mechanics of Materials Proof of concept Modeling and Simulation Key (cryptography) lcsh:Materials of engineering and construction. Mechanics of materials Artificial intelligence 0210 nano-technology business |
| Zdroj: | NPJ Computational Materials, 6, 106 npj Computational Materials, 6(1):106. Nature Publishing Group npj Computational Materials, Vol 6, Iss 1, Pp 1-10 (2020) |
| ISSN: | 2057-3960 |
| DOI: | 10.1038/s41524-020-00375-7 |
| Popis: | In recent years, artificial intelligence (AI) methods have prominently proven their use in solving complex problems. Across science and engineering disciplines, the data-driven approach has become the fourth and newest paradigm. It is the burgeoning of findable, accessible, interoperable, and reusable (FAIR) data generated by the first three paradigms of experiment, theory, and simulation that has enabled the application of AI methods for the scientific discovery and engineering of compounds and materials. Here, we introduce a recipe for a data-driven strategy to speed up the virtual screening of two-dimensional (2D) materials and to accelerate the discovery of new candidates with targeted physical and chemical properties. As a proof of concept, we generate new 2D candidate materials covering an extremely large compositional space, downselect 316,505 likely stable 2D materials, and predict the key physical properties of these new 2D candidates. Finally, we hone in on the most propitious candidates of functional 2D materials for energy conversion and storage. |
| Databáze: | OpenAIRE |
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