| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Discovery and Design of Materials
|
| Presentation Title |
Adaptive Discovery and Mixed-variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials |
| Author(s) |
Wei Chen, Hengrui Zhang |
| On-Site Speaker (Planned) |
Hengrui Zhang |
| Abstract Scope |
Materials design is challenged by high-dimensionality of the composition–structure space and mixed qualitative and quantitative design variables. We propose a machine learning assisted adaptive design framework and demonstrate it in the autonomous search for metal-insulation transition (MIT) materials. Starting with natural language processing methods, we extract from the literature the potential materials families exhibiting MITs. Next, we use active learning and first-principles calculations to virtually screen these materials, building a classier and a database for MIT materials. Within two prominent MIT families, we conduct Bayesian Optimization-based adaptive discovery driven by a novel latent variable Gaussian process (LVGP) model, which can predict material properties from the composition, that contains categorical design variables (e.g., elements). Using this framework, we have discovered several potential MIT materials not previously reported, for which experimental syntheses are in process. Our framework can be extended to accelerate materials design beyond MITs. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, |