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Condensed Matter > Materials Science

arXiv:1903.09385 (cond-mat)
[Submitted on 22 Mar 2019 (v1), last revised 8 May 2019 (this version, v2)]

Title:Bayesian optimization of chemical composition: a comprehensive framework and its application to $R$Fe$_{12}$-type magnet compounds

Authors:Taro Fukazawa, Yosuke Harashima, Zhufeng Hou, Takashi Miyake
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Abstract:We propose a framework for optimization of the chemical composition of multinary compounds with the aid of machine learning. The scheme is based on first-principles calculation using the Korringa-Kohn-Rostoker method and the coherent potential approximation (KKR-CPA). We introduce a method for integrating datasets to reduce systematic errors in a dataset, where the data are corrected using a smaller and more accurate dataset. We apply this method to values of the formation energy calculated by KKR-CPA for nonstoichiometric systems to improve them using a small dataset for stoichiometric systems obtained by the projector-augmented-wave (PAW) method. We apply our framework to optimization of $R$Fe$_{12}$-type magnet compounds (R$_{1-\alpha}$Z$_{\alpha}$)(Fe$_{1-\beta}$Co$_{\beta}$)$_{12-\gamma}$Ti$_{\gamma}$, and benchmark the efficiency in determination of the optimal choice of elements (R and Z) and ratio ($\alpha$, $\beta$ and $\gamma$) with respect to magnetization, Curie temperature and formation energy. We find that the optimization efficiency depends on descriptors significantly. The variable $\beta$, $\gamma$ and the number of electrons from the R and Z elements per cell are important in improving the efficiency. When the descriptor is appropriately chosen, the Bayesian optimization becomes much more efficient than random sampling.
Comments: 16 pages, 13 figures
Subjects: Materials Science (cond-mat.mtrl-sci)
Cite as: arXiv:1903.09385 [cond-mat.mtrl-sci]
  (or arXiv:1903.09385v2 [cond-mat.mtrl-sci] for this version)
  https://doi.org/10.48550/arXiv.1903.09385
arXiv-issued DOI via DataCite
Journal reference: Phys. Rev. Materials 3, 053807 (2019)
Related DOI: https://doi.org/10.1103/PhysRevMaterials.3.053807
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Submission history

From: Taro Fukazawa [view email]
[v1] Fri, 22 Mar 2019 07:27:32 UTC (2,826 KB)
[v2] Wed, 8 May 2019 08:02:44 UTC (2,745 KB)
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