期刊
MATTER
卷 3, 期 2, 页码 433-448出版社
CELL PRESS
DOI: 10.1016/j.matt.2020.04.016
关键词
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资金
- Samsung Advanced Institute of Technology
- Ontario Research Foundation
- Natural Sciences and Engineering Research Council of Canada
- Canada Foundation for Innovation under the Compute Canada
- Government of Ontario
- Ontario Research Fund - Research Excellence
- University of Toronto
- Federal Economic Development Agency of Southern Ontario
- Province of Ontario
- IBM Canada
- Ontario Centers of Excellence
- Mitacs
- MCF program
- AI4D program
Mapping materials science problems onto computational frameworks suitable for machine learning can accelerate materials discovery. Combining proposed crystal site feature embedding (CSFE) representation with convolutional and extensive deep neural networks, we achieve a low mean absolute test error of 3.7 meV/atom and 0.069 eV on density functional theory energies and band gaps of mixed halide perovskites. We explore how a small amount of cadmium doping can potentially be applied in solar cell design and sample the large chemical space by using a variational autoencoder to discover interesting perovskites with band gaps in the ultraviolet and infrared. Additionally, we use CSFE to explore chemical spaces and small doping concentrations beyond those used for training. We further show that CSFE has a mean absolute test error of 7 meV/atom and 0.13 eV for total energies and band gaps for 2D perovskites and discuss its adaptability for exploration of an even wider variety of chemical systems.
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