Theoretical and computational methods for accelerated materials discovery
ORAL
Abstract
Predicting properties of materials and phase transformation using theoretical and computational multi-scale methods involving artificial intelligence and machine learning is important and highly rewarding. We investigate reliability of the relevant methods, apply them to caloric materials and high-entropy alloys, and demonstrate how theoretical guidance for experiment accelerates materials discovery.
*We acknowledge support by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences, Materials Science and Engineering Division. Consideration of phase transitions in caloric materials is partially supported by the U.S. DOE, Advanced Manufacturing Office of the Office of Energy Efficiency and Renewable Energy, through CaloriCool(TM). Ames Laboratory is operated for DOE by Iowa State University under Contract No. DE-AC02-07CH11358.
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Presenters
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Nikolai A Zarkevich
- Ames Laboratory, Iowa State University