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Fine-grained optimization method for crystal structure prediction
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- Terayama, Kei
- Graduate school of Frontier Sciences, the University of Tokyo・RIKEN Center for Advanced Intelligence Project・Graduate school of Medicine, Kyoto University
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- Yamashita, Tomoki
- Center for Materials research by Information Integration, Research and Services, Division of Materials Data and Integrated System, National Institute for Materials, Tsukuba・Institute of Scientific and Industrial Research, Osaka University
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- Oguchi, Tamio
- Center for Materials research by Information Integration, Research and Services, Division of Materials Data and Integrated System, National Institute for Materials, Tsukuba・Institute of Scientific and Industrial Research, Osaka University
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- Tsuda, Koji
- Graduate school of Frontier Sciences, the University of Tokyo・Graduate school of Frontier Sciences, the University of Tokyo・Center for Materials research by Information Integration, Research and Services, Division of Materials Data and Integrated System, National Institute for Materials, Tsukuba
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Description
Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures. Relaxing a structure requires multiple optimization steps. It is time consuming to fully relax all the initial structures, but it is difficult to figure out which initial structure leads to the optimal solution in advance. In this paper, we propose a optimization method for crystal structure prediction, called Look Ahead based on Quadratic Approximation, that optimally assigns optimization steps to each candidate structure. It allows us to identify the most stable structure with a minimum number of total local optimization steps. Our simulations using known systems Si, NaCl, Y₂Co₁₇, Al₂O₃, and GaAs showed that the computational cost can be reduced significantly compared to random search. This method can be applied for controlling all kinds of local optimizations based on first-principles calculations to obtain best results under restricted computational resources.
Journal
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- npj Computational Materials
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npj Computational Materials 4 32-, 2018-07-10
Springer Nature
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Details 詳細情報について
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- CRID
- 1050564288161154944
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- NII Article ID
- 120006532090
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- ISSN
- 20573960
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- HANDLE
- 2433/234727
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- IRDB
- Crossref
- CiNii Articles
- OpenAIRE