Bayesian optimization of chemical composition: A comprehensive framework and its application to R Fe12 -type magnet compounds
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 method. We apply our framework to optimization of R Fe12 -type magnet compounds (R1 -αZα)(Fe1-βCoβ) 12 -γTiγ , and benchmark the efficiency in determination of the optimal choice of elements (R and Z ) and ratio (α , β , and γ ) with respect to magnetization, Curie temperature, and formation energy. We find that the optimization efficiency depends on descriptors significantly. The variables β , γ , 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.
- Publication:
-
Physical Review Materials
- Pub Date:
- May 2019
- DOI:
- 10.1103/PhysRevMaterials.3.053807
- arXiv:
- arXiv:1903.09385
- Bibcode:
- 2019PhRvM...3e3807F
- Keywords:
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- Condensed Matter - Materials Science
- E-Print:
- 16 pages, 13 figures