Synthesis‐condition recommender system discovers novel inorganic oxides
Author:
Affiliation:
1. Department of Materials Science and Engineering Kyoto University Sakyo Kyoto Japan
2. JST, PRESTO Kawaguchi Japan
3. Nanostructures Research Laboratory Japan Fine Ceramics Center Nagoya Japan
Funder
Precursory Research for Embryonic Science and Technology
Publisher
Wiley
Subject
Materials Chemistry,Ceramics and Composites
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/jace.18113
Reference23 articles.
1. Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
2. Accelerating materials property predictions using machine learning;Pilania G;Sci Rep,2013
3. Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
4. Informatics-aided bandgap engineering for solar materials
5. How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Recommender system for discovery of inorganic compounds;npj Computational Materials;2022-10-13
2. Coordination polymers as precursors of simple and complex oxides;International Journal of Applied Ceramic Technology;2022-08-11
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