Leveraging FAIR Data to Accelerate the Discovery of Alloys with Optimal Melting Temperatures Using Active Learning and Molecular Dynamics Simulations
FAIR data and workflows can significantly accelerate materials discovery by enabling efficient reuse of prior knowledge and optimization of simulation parameters, as demonstrated by a 10x speedup in identifying alloys with optimal melting temperatures using active learning and molecular dynamics simulations.