Deep learning framework for material design space exploration using active transfer learning and data augmentation

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Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.
Publisher
NATURE PORTFOLIO
Issue Date
2021-09
Language
English
Article Type
Article
Citation

NPJ COMPUTATIONAL MATERIALS, v.7, no.1

ISSN
2057-3960
DOI
10.1038/s41524-021-00609-2
URI
http://hdl.handle.net/10203/287877
Appears in Collection
ME-Journal Papers(저널논문)
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