Metadynamics enhanced training Datasets, DFT-SCAN accurate GAP Model and MD trajectories for "AL4GAP: Active Learning Workflow for generating DFT-SCAN Accurate Machine-Learning Potentials for Combinatorial Molten Salt Mixtures"

This material is based upon work supported by Laboratory Directed Research and Development (LDRD-CLS-1-630) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research was in portion supported by ExaLearn Co-design Center of the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. Portions of this work were sponsored by the U.S. Department of Energy, Office of Nuclear Energy’s Material Recovery and Wasteform Development Program under contract DE-AC02-06CH11357. We gratefully acknowledge the computing resources provided on Bebop; a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. This research used resources of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. C.B acknowledges support from the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy under Contract No. 89233218CNA000001.