| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Efficient Navigation of the Search Space for Accelerated Materials Discovery |
| Author(s) |
Prasanna V. Balachandran |
| On-Site Speaker (Planned) |
Prasanna V. Balachandran |
| Abstract Scope |
Computational strategies that enable efficient navigation of the vast materials search space have the potential to accelerate the discovery of new materials. This is especially critical when brute-force evaluation of the search space is prohibitively expensive. In this talk, I will focus on examples where we have shown the synergistic integration of density functional theory (DFT) calculations, machine learning (ML), optimal learning and experimental data to rationally guide future experiments in search of new materials with targeted properties. The role of ML is two-fold: (i) to establish a relationship between the features and property of interest and (ii) to quantify prediction uncertainties. The optimal design, on the other hand, uses the ML outcome to recommend the next promising experiment for validation and feedback. Data from DFT calculations are used to construct descriptors for ML or to validate ML predictions to provide confidence in the data-driven approach, prior to performing experiments. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |