Overview of the active learning framework for developing high entropy alloys. The framework combines machine learning models, density functional theory-based calculations, thermodynamic simulations, and experimental feedback.
In a pilot project, machine learning is helping to develop materials for hydrogen storage, for example. Overview of the active learning framework for developing high entropy alloys. The framework combines machine learning models, density functional theory-based calculations, thermodynamic simulations, and experimental feedback. Science 378 (2022) 6615. abo4940 - Artificial intelligence is opening up new possibilities in the development of new materials. Particularly in the search for materials for special applications such as high-entropy alloys, which contain several components in roughly equal proportions, machine learning could support research. An international team led by the Max Planck Institute for Iron Research is demonstrating this in its search for invar alloys for the storage of hydrogen, ammoia or natural gas.
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