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Training Dataset and GAP Model for Molten LiCl Melts

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journal contribution
posted on 2022-07-18, 11:53 authored by Ganesh SivaramanGanesh Sivaraman, Jicheng Guo, Logan Ward, Nathaniel Hoyt, Mark Williamson, Ian FosterIan Foster, Chris Benmore, Nicholas Jackson

Training/ validation dataset and Gaussian Approximation Potential model for molten LiCl melts reported in Sivaraman et al., J. Phys. Chem. Lett. 2021, 12, 4278−4285


This material is based upon work supported by Lab- oratory Directed Research and Development (LDRD- 2020-0226, LDRD-CLS-1-630) funding from Argonne Na- tional Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Con- tract No. DE-AC02-06CH11357.

This research was sup- ported 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

We gratefully acknowledge the computing resources provided on Bebop; a high-performance computing cluster oper- ated 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. HEXRD measurements were made on beamline 6-ID-D at the Advanced Pho- ton Source, a U.S. Department of Energy (DOE) Of- fice of Science User Facility operated for the DOE Of- fice of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Argonne National Laboratory’s work was supported by the U.S. Depart- ment of Energy, Office of Science, under contract DE- AC02-06CH11357