Data-Driven Materials Exploration for Li-Ion Conductive Ceramics by Exhaustive and Informatics-Aided Computations
Corresponding Author
Prof. Dr. Masanobu Nakayama
Frontier Research Institute for Materials Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Elements Strategy Initiative for Catalysts and Batteries, Kyoto University f1-30 Goryo-Ohara, Nishikyo-ku, Kyoto, 615-8245 Japan
National Institute for Materials Science, Global Research Center for Environment and Energy based on Nanomaterials Science (NIMS-GREEN) 1-1 Namiki, Tsukuba, Ibaraki, 305-0044 Japan
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Search for more papers by this authorKenta Kanamori
Department of Computer Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Search for more papers by this authorKoki Nakano
Frontier Research Institute for Materials Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Search for more papers by this authorDr. Randy Jalem
National Institute for Materials Science, Global Research Center for Environment and Energy based on Nanomaterials Science (NIMS-GREEN) 1-1 Namiki, Tsukuba, Ibaraki, 305-0044 Japan
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Japan Science and Technology Agency (JST), PRESTO 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012 Japan
Search for more papers by this authorProf. Dr. Ichiro Takeuchi
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Department of Computer Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
Search for more papers by this authorDr. Hisatsugu Yamasaki
Battery Material Engineering & Research Div., Toyota Motor Corporation 1200, Mishuku, Susono, Shizuoka, 410-1193 Japan
Search for more papers by this authorCorresponding Author
Prof. Dr. Masanobu Nakayama
Frontier Research Institute for Materials Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Elements Strategy Initiative for Catalysts and Batteries, Kyoto University f1-30 Goryo-Ohara, Nishikyo-ku, Kyoto, 615-8245 Japan
National Institute for Materials Science, Global Research Center for Environment and Energy based on Nanomaterials Science (NIMS-GREEN) 1-1 Namiki, Tsukuba, Ibaraki, 305-0044 Japan
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Search for more papers by this authorKenta Kanamori
Department of Computer Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Search for more papers by this authorKoki Nakano
Frontier Research Institute for Materials Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
Search for more papers by this authorDr. Randy Jalem
National Institute for Materials Science, Global Research Center for Environment and Energy based on Nanomaterials Science (NIMS-GREEN) 1-1 Namiki, Tsukuba, Ibaraki, 305-0044 Japan
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Japan Science and Technology Agency (JST), PRESTO 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012 Japan
Search for more papers by this authorProf. Dr. Ichiro Takeuchi
Center for Materials research by Information Integration (CMI 2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science – “Materials research by Information Integration” Initiative (NIMS-Mi2i), 1-2-1 Sengen, Tsukuba-city Ibaraki, 305-0047 Japan
Department of Computer Science, Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555 Japan
RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
Search for more papers by this authorDr. Hisatsugu Yamasaki
Battery Material Engineering & Research Div., Toyota Motor Corporation 1200, Mishuku, Susono, Shizuoka, 410-1193 Japan
Search for more papers by this authorAbstract
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.
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