Journal
SCIENTIFIC REPORTS
Volume 8, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-018-23852-y
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Funding
- Materials research by Information Integration Initiative (MI2I)
- Development Program of the Japan Science and Technology Agency (JST) under Advanced Materials Informatics through Comprehensive Integration among Theoretical, Experimental, Computational and Data-centric Sciences research area
- Elements Strategy Initiative to Form Core Research Center, Ministry of Education Culture, Sports, Science and Technology (MEXT)
- JST Precursory Research for Embryonic Science and Technology (PRESTO) program
- MEXT KAKENHI [17H00758]
- RIKEN Center for Advanced Intelligence Project
- JST CREST-FS
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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 (E-b). We demonstrated this on 318 tavorite-type Li- and Na-containing compounds. We found that the scheme only requires similar to 30% of the total DFT-E-b evaluations on the average to recover the optimal compound similar to 90% of the time. Its recovery performance for desired compounds in the tavorite search space is similar to 2x more than random search (i.e., for E-b < 0.3 eV). Our approach offers a promising way for addressing computational bottlenecks in large-scale material screening for fast ionic conductors.
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