| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Generative Deep Neural Networks for Inverse Materials Design using Backpropagation and Adaptive Learning |
| Author(s) |
Grace X. Gu, Chun-Teh Chen |
| On-Site Speaker (Planned) |
Grace X. Gu |
| Abstract Scope |
Machine learning (ML) has shown great potential to accelerate materials discovery and design processes in a manifold way. Most ML-based investigations were dedicated to training models to predict properties of interests from material descriptors. After training, ML models were used as filters to explore the material design space in a brute-force manner. However, this brute-force screening methodology can only be applied to problems with a small design space. Here, we present Generative Inverse Design Networks (GIDNs), a general-purpose inverse design approach using backpropagation and adaptive learning. The novelty of the proposed approach is that it is integrated with random search in the design process to overcome the local minimum problem paired with adaptive learning to improve the performance of superior designs and to reduce the amount of training data needed to do so. Results show that GIDNs outperform other common optimization methods including gradient-based topology optimization and genetic algorithms. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |