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 limi...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.展开更多
基金This research was supported by Basic Science Research Program(2019R1A2C4070690)Creative Materials Discovery Program(2016M3D1A1900038)through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)of the Republic of Korea,as well as the KAIST-funded Global Singularity Research Program for 2019(N11190118).Additionally,the authors acknowledge funding support from 3M and Inc.to HP Labs.
文摘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.
