| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
AI-based High-Throughput Screening Framework for Battery Materials Design |
| Author(s) |
Alina Negoita, Nasim Souly, Alina Negoita, Prateek Agrawal, Christian Tae, Vedran Glavas, Julian Wegener, Kai Gerstner, Alex Alekseyenko |
| On-Site Speaker (Planned) |
Alina Negoita |
| Abstract Scope |
Identification of the optimal combination of parameters in materials design requires large effort since many possible candidates have to be evaluated by experiments or simulations. Creating synthetic materials already decreases the effort significantly, but one still needs to focus on a reduced selection of material combinations. We propose a data-driven high-throughput screening framework for materials design that allows materials screening of millions of candidates in only milliseconds per prediction. We demonstrate the feasibility of our approach on the design of battery cathode materials combining synthetic microstructure generation and electrochemical modeling considering also battery cell properties. We apply simple Machine Learning models on averaged microstructure properties for materials screening and complex Deep Learning models on the 3D microstructures to enable generative materials design. We use model uncertainty to efficiently create new simulation data samples for incremental model improvement. The most promising candidates selected by materials screening are then validated with simulations. |
| Proceedings Inclusion? |
Definite: Other |