Safe and robust batteries are urgently requested today for power sources of electric vehicles. Thus, a growing interest has been noted for fabricating those with solid electrolytes. Materials search by density functional theory (DFT) methods offers great promise for finding new solid electrolytes but the evaluation is known to be computationally expensive, particularly on ion migration property. In this work, we proposed a Bayesian-optimization-driven DFT-based approach to efficiently screen for compounds with low ion migration energies (. We demonstrated this on 318 tavorite-type Li- and Na-containing compounds. We found that the scheme only requires ~30% of the total DFT- evaluations on the average to recover the optimal compound ~90% of the time. Its recovery performance for desired compounds in the tavorite search space is ~2× more than random search (i.e., for  < 0.3 eV). Our approach offers a promising way for addressing computational bottlenecks in large-scale material screening for fast ionic conductors.

Interest in all‐solid‐state Li‐ion batteries (LIBs) using non‐flammable Li‐conducting ceramics as solid electrolytes has increased, as safe and robust batteries are urgently desired as power sources for (hybrid) electric vehicles. However, the low Li‐ion conductivities of ceramics have hindered all‐solid‐state LIB commercialization; many researchers have attempted to develop fast Li‐ion conductors. We introduce two efficient high‐throughput computational approaches for materials exploration: (i) exhaustive search and (ii) informatics‐aided prediction. For demonstration, ∼400 Li‐ and Zn‐containing oxide (Li−Zn−X−O) compounds of varied crystal structures are extracted from Materials Project datasets. We calculate the migration energies for Li‐ion conduction and the phase stabilities (decomposition energies) of these materials by simulation and apply Bayesian optimization to determine the material with the highest ionic conductivity. The results show much greater efficiency than a random search algorithm.

We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for the atomic transport. The proposed method generates numerous random samples of the entire potential energy surface (PES) from a probabilistic Gaussian process model of the PES, which enables defining the likelihood of the dominant points. The robustness and efficiency of the method are demonstrated on a dozen model cases for proton diffusion in oxides, in comparison with a conventional nudge elastic band method. PACS numbers: 31.50.Bc,66.30.Lw,89.20.Ff Atomic transport plays a key role in a variety of phenomena related to physics, chemistry, and materials science. Concerning the transport of a mobile atom governed by thermally activated processes in a crystal, the kinetics is fully characterized by the entire potential energy surface (PES) of the mobile atom in the host crystal. The most important region in the entire PES is the optimal path, which is defined as the lowest-energy path between two global minimum points separated by a lattice translation vector. Therefore, the optimal path is identical to a valley line, which generally passes through several saddle points. Based on transition state theory (TST) [1][2][3][4], the kinetics is determined primarily by the potential barrier of the optimal path, i.e., the difference in potential energy (PE) between the global minimum point and the bottleneck point, which is defined as the point having the highest PE on the optimal path.The nudged elastic band (NEB) method [5, 6] is a wellestablished and powerful technique for identifying the optimal path and its energy profile. The NEB method, however, has a serious drawback in that it requires a given initial trajectory to identify the optimal path. When the optimal path consists of several elementary paths, all local minimum points (including the global minimum point) in the entire PES are found in advance, because these points may be the initial and final points of the elementary paths. For each of the possible initial trajectories derived from the local minimum points, an elementary path is analyzed using the NEB method, and the optimal path formed by some of the elementary paths is finally identified. Moreover, physical and chemical prior knowledge, e.g., ionic radii, chemical bonding states, and electrostatic interaction, has usually been used to obtain local minimum points and initial trajectories. However, the excessive dependence on the prior knowledge may cause us to miss a key elementary path. Thus, a robust and efficient alternative method to the NEB-based analysis is desired.In the present study, we propose a machine-learning (ML) method for robustly and efficiently estimating the potential barrier of the optimal path. The basic strategy is to focus only on finding the two dominant points, i.e., the global minimum and bottleneck points. To this end, a probabilistic Gaussian process (GP) model [7,8] of the PES is introduced, and is iteratively updat...

We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier is partially evaluated by first-principles calculations. To preferentially evaluate the region of interest governing the atomic diffusivity, a statistical PES model based on a Gaussian process (GP-PES) is constructed and updated iteratively from known information on already-computed potential energies (PEs). In the proposed method, all local energy minima (stable & metastable sites) and elementary processes of atomic diffusion (atomic jumps) are explored on the predictive mean of the GP-PES. The uncertainty of jump frequency in each elementary process is then estimated on the basis of the variance of the GP-PES. The acquisition function determining the next grid point to be computed is designed to reflect the impacts of the uncertainties of jump frequencies on the uncertainty of the macroscopic atomic diffusivity. The numerical solution of the master equation is here employed to readily estimate the atomic diffusivity, which enables us to design the acquisition function reflecting the centrality of each elementary process.

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