About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Machine Learning of Phase Diagrams: Applications to Energy Materials |
Author(s) |
Jarrod Lund, Haoyue Wang, Richard Braatz, Edwin Garcia |
On-Site Speaker (Planned) |
Edwin Garcia |
Abstract Scope |
By starting from experimental- and ab initio-determined phase diagrams (PDs) of materials, a machine learning (ML) method is developed to infer the free energy function for each phase. The method samples the multidimensional space of material parameters and user-defined physical constraints into a database of millions of PDs in order to identify the target material properties. The method presented herein is 1000x to 100,000x faster than currently available approaches, and defines a new paradigm on the quantification of properties of materials and devices. As an example application, the developed methodology is combined with the most widely used thermodynamic models – regular solution, Redlich-Kister, and sublattice formalisms– to infer the properties of materials for lithium-ion battery applications. Applications to battery materials, such as LCO and LFP, and liquid electrolytes such as the EC-DMC-PC systems are presented. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Energy Conversion and Storage, Other |