Artificial Intelligence and Machine Learning for materials
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Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Materials Characterization Techniques · X-ray Diffraction in Crystallography
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References (21)
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1. Machine Learning in materials science
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7. Recent advances and applications of deep learning methods in materials science10.1038/s41524-022-00734-6 ↗
8. K. Yao, Y. Zheng, Nanophotonics and Machine Learning, Springer, Cham, 2023. doi: 10.1007/978-3-031-20473-9.10.1007/978-3-031-20473-9 ↗
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10. From DFT to machine learning: recent approaches to materials science–A review10.1088/2515-7639/ab084b ↗
11. DScribe: Library of descriptors for machine learning in materials science10.1016/j.cpc.2019.106949 ↗
12. A strategy to apply machine learning to small datasets in materials science10.1038/s41524-018-0081-z ↗
13. C. Molnar, Interpretable Machine Learning, https://christophmolnar.com/, 2024 (accessed Sep. 17, 2024).
14. The role of machine learning in the understanding and design of materials10.1021/jacs.0c09105 ↗
15. Machine learning in materials research: Developments over the last decade and challenges for the future10.1016/j.cossms.2024.101189 ↗
16. Advancing materials science through next-generation machine learning10.1016/j.cossms.2024.101157 ↗
17. Machine learning models in phononic metamaterials10.1016/j.cossms.2023.101133 ↗
18. Pushing the limits of multifunctional metasurface by deep learning10.1016/j.cossms.2024.101163 ↗
19. Mapping information and light: Trends of AI-enabled metaphotonics10.1016/j.cossms.2024.101144 ↗
20. Advancing programmable metamaterials through machine learning-driven buckling strength optimization10.1016/j.cossms.2024.101161 ↗
21. Deep learning for nano-photonic materials – The solution to everything!?10.1016/j.cossms.2023.101129 ↗