.png){kind=icon}
返回
Deep learning framework for material design space exploration using active transfer learning and data augmentation
.png){kind=icon}
OA
AI
DOI：10.1038/s41524-021-00609-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
TOPOLOGY OPTIMIZATION
COMPOSITE-MATERIALS
PHASE-FIELD
SIMULATION
DISCOVERY
MODELS
期刊
IF:
11.9
论文数:
2.4K
被引数:
1.7W
机构
引用论文
Molecular generative model based on conditional variational autoencoder for de novo molecular design
OAAI
OAAI分享收藏Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment.png)
Materials HorizonsOAAI
Materials HorizonsIF10.7
OAAI分享收藏Bone-Inspired Materials by Design: Toughness Amplification Observed Using 3D Printing and TestingOAAI
OAAI分享收藏
查看更多内容