@article{ecdbe8a41e3142e195901d92b9696d60,
title = "Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction",
abstract = "Unraveling the liquid structure of multicomponent molten salts is challenging due to the difficulty in conducting and interpreting high-temperature diffraction experiments. Motivated by this challenge, we developed composition-transferable Gaussian approximation potential (GAP) for molten LiCl-KCl. A DFT-SCAN accurate GAP is active-learned from only ∼1100 training configurations drawn from 10 unique mixture compositions enriched with metadynamics. The GAP-computed structures show strong agreement across high-energy x-ray diffraction experiments, including for a eutectic not explicitly included in model training, thereby opening the possibility of composition discovery.",
author = "Jicheng Guo and Logan Ward and Yadu Babuji and Nathaniel Hoyt and Mark Williamson and Ian Foster and Nicholas Jackson and Chris Benmore and Ganesh Sivaraman",
note = "This material is based on work supported by Laboratory Directed Research and Development (Grants No. LDRD-2020-0226 and No. 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 supported by ExaLearn Co-design Center of the Exascale Computing Project (Grant No. 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 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 No. DE-AC02-06CH11357. HEXRD measurements were made on beamline 6-ID-D at 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 No. DE-AC02-06CH11357. G.S. would like to thank Professor Gabor Cs{\'a}nyi for constructive feedback on the manuscript and fruitful discussions on GAP model fitting. G.S. would like to thank Dr. Anand Narayanan Krishnamoorthy for fruitful discussions on local/bulk partition coefficients.",
year = "2022",
month = jul,
day = "1",
doi = "10.1103/PhysRevB.106.014209",
language = "English (US)",
volume = "106",
journal = "Physical Review B",
issn = "2469-9950",
publisher = "American Physical Society",
number = "1",
}