Interpretable discovery of new semiconductors with machine learning

• URL: http://arxiv.org/abs/2101.04383v1
• Date: Tue, 12 Jan 2021 10:23:16 GMT
• Title: Interpretable discovery of new semiconductors with machine learning
• Authors: Hitarth Choubisa (1), Petar Todorovi\'c (1), Joao M. Pina (1), Darshan H. Parmar (1), Ziliang Li (1), Oleksandr Voznyy (4), Isaac Tamblyn (2,3), Edward Sargent (1) ((1) Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada, (2) National Research Council of Canada, Ottawa, ON, Canada, (3) Vector Institute for Artificial Intelligence, Toronto, ON, Canada, (4) Department of Physical and Environmental Sciences, University of Toronto, Scarborough, ON, Canada)
• Abstract summary: We report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN) The strategy enables efficient search over the materials space of 10$8$ ternaries and 10$11$ quaternaries$7$ for candidates with target properties.
• Score: 10.09604500193621
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on high-throughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The strategy enables efficient search over the materials space of ~10$^8$ ternaries and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B-site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans.

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