Research Interests

Data-Driven Discovery with Machine Learning and DFT
使用机器学习和DFT进行数据驱动的发现
Renewable Energy and Solid-State Technologies
可再生能源和固态技术
Simulation and Modelling of Materials
材料的模拟与建模
Title: Associate Professor
Email: leeburton@shu.edu.cn
Website: http://icqms.shu.edu.cn/Lee.html
Highlighted publications:

L. A. Burton et al. High-throughput identification of electrides from all known inorganic materials. Chemistry of Materials, 2018, 30(21): 7521. click link
Y. Hinuma et al. Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nature Communications, 2016, 7: 11962. click link
J. M. Skelton et al. Anharmonicity in the High-Temperature Phase of SnSe: Soft Modes & Three-Phonon Interactions. Physical Review Letters 2016, 117(7): 075502. click link

Projects:
Projects A: Machine learning prediction of new materials for renewable energy.
Projects B: Searching all known materials to uncover new behaviors.
Projects C: New methods for modelling materials for industry applications.