MIT's information-theoretic ML approach beats brute-force alloy training
MIT researchers report a new way to build training datasets for machine-learning interatomic potentials, using information theory to maximize chemical diversity instead of brute-force computation. According to the team, the resulting models predict material properties for chemically disordered metal alloys more accurately than prior sampling strategies and the