Abstract
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.
Highlights
In order to discover or design novel materials having outstanding properties, significant effort has been paid to devise various material design approaches such as biomimicry, design of experiment methods, and other conventional optimization methods[1,2,3,4,5,6,7,8,9,10,11,12,13]
deep neural network (DNN) trained with the initial training dataset is capable of making a reliable prediction on the design space slightly larger than the training data domain, as represented in the bluish region
To find the materials with desired properties, which are positioned outside the domain of initial training data, DNN should be able to make a reliable prediction on the domain containing the desired design
Summary
In order to discover or design novel materials having outstanding properties, significant effort has been paid to devise various material design approaches such as biomimicry, design of experiment methods, and other conventional optimization methods[1,2,3,4,5,6,7,8,9,10,11,12,13]. These approaches often require in-depth physics-based analysis of the relationship between materials descriptors and properties. It requires a lot of effort to find desired materials in vast design space with a forward design approach, because a large number of candidates must be tested to search for the optimal material due to the absence of the gradient of predicted output with respect to input features[19,30,31,32]
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