Elimination of Detrimental Grain Boundary Segregation in Garnets

Datasets for training machine-learning interatomic potentials for simulating disordered geometries and mechanical response simulations for Al-/Ta-doped Li7Zr2La3O12 solid electrolytes, as well as simulation structures for Al-/Ta-doped Li7Zr2La3O12 solid electrolytes, published in a paper "Elimination of Detrimental Grain Boundary Segregation in Garnets" by Y. Kai et al. in Nature Communications. Data format for training dataset is based on an input file for n2p2 code (https://compphysvienna.github.io/n2p2/). Open Distribution Dataset: LLNL-DATA-2019875 / LLNL-DATA-2018982 The data was produced under the auspices of the U.S. Department of Energy (DOE) by the Lawrence Livermore National Laboratory (DE-AC52-07NA27344) and was sponsored by the U.S. DOE Transportation Technologies Office. Computational resources were sponsored by the U.S. DOE's Office of Critical Minerals and Energy Innovation located at the National Laboratory of the Rockies and the Computing Grand Challenge program from Lawrence Livermore National Laboratory.