Machine Learning for High-Throughput Configuration Sampling of Li−La−Ti−O Disordered Solid-State Electrolyte

Most solid-state lithium electrolytes are disordered ionic crystalline materials that possess crystallographic sites that can be vacant or occupied by different ions. The presence of these partially occupied sites enables lithium diffusion through their lattice and makes such materials promising for developing all-solid batteries. High-throughput computational screening of such materials must bypass costly DFT sampling of disordered configurations and, therefore, commonly relies on the computationally efficient Coulomb approximation to find just a few representative low-energy ionic configurations, for which DFT is then used to quickly predict a number of important materials' properties, such as the electrochemical stability window. This work demonstrates, using the Li−La−Ti−O solid electrolyte (LLTO) as an example, that the Coulomb approximation fails to correctly detect the most stable arrangement of Li and La ions in the LLTO, which has a noticeable impact on the accuracy of subsequent computational prediction of the electrochemical stability window of the material. The analysis herein shows that the sampling problem arises from the relatively modest geometry relaxation of the LLTO lattice. A kernel ridge regression machine learning (ML) method employing the smooth overlap of atomic positions as a structure descriptor (SOAP-KRR) leads to significant improvements in detecting the most stable configurations of the LLTO. The universal ML potential based on the multiple atomic cluster expansion is also found to be reliable but to a lesser extent than SOAP-KRR. Remarkably, accurate energies can be obtained with SOAP-RKK trained on as few as 40 LLTO structures, making this method promising for designing force matching ML potentials that can serve as a computationally inexpensive alternative to the costly DFT structure relaxation in high-throughput screening of large data sets of ionic materials.