Project title: Materials Informatics of Multi-Component Metal Oxides

Supervisors: Aron Walsh and Keith Butler

Project description:
Metal oxides encompass a wide family of materials, including insulators, metals, semiconductors, and superconductors. Their physical properties are intimately connected to their chemical composition and crystal structure; however, relatively few structure-property relationships have been established to date. This project aims to deepen our understanding of metal oxides by combining the latest advances in materials theory and simulation, bridging first-principles methods, data-mining, and statistical models through machine learning [1]. It will build on recent work developing the code SMACT [2,3]. A particular focus will be on systems combining more than one metal (ternary and quaternary compounds) and those adopting non-centrosymmetric crystal structures (piezoelectric and ferroelectric compounds) with potential for energy conversion applications.

1. Machine learning for molecular and materials science, Nature 559, 547 (2018)
2. Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure, Chemical Science 9, 1022 (2018)
3. SMACT: Semiconducting Materials by Analogy and Chemical Theory, The Journal of Open Source Software 4(38), 1361 (2019)