| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Discovery and Design of Materials
|
| Presentation Title |
An Inverse Materials Design Route Based on Structure-property Linkages Leveraging 3D Convolutional Neural Network and Bayesian Optimization |
| Author(s) |
Xiao Shang, Yu Zou |
| On-Site Speaker (Planned) |
Xiao Shang |
| Abstract Scope |
Material properties are macroscale expressions of material microstructures, and the key to materials design lies in identifying the underlying structure-property (SP) linkages. Compared with conventional heuristic- and simulation-based methods, advanced Machine Learning (ML)- based techniques are advantageous for its high accuracy and extremely low computation time, enabling fast high-throughput materials design. In this work, we propose a general route for inverse materials design, where realistic microstructures can be identified with target mechanical properties such as yield strengths. 3D Convolutional Neural Networks (CNNs) are used to mine SP linkages from synthesised microstructures datasets, after which Bayesian Optimization (BO) is used for inversely identifying the optimal microstructures expressing desired mechanical properties. Titanium alloy (Ti6-Al4-V) is used to demonstrate the design route, which is generalizable to other materials systems. Our design route provides a reliable and computational efficient way to achieve “Materials-by-Design” for guiding the design and manufacturing of next generation high-performing materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, Mechanical Properties |