Objectives
To facilitate discovery and optimization of novel constituent materials and modules for energy storage, we aim to develop machine learning-based models and concomitant databases.
This enterprise encompasses a diverse array of projects involving applications of machine learning methods and big data.
Areas of Research
Among the priority venues are the following:
- Big data analytics for establishing structure-property relationships in materials for battery applications
- Applications of machine learning methods for large-scale quantum and classical molecular dynamics simulations of molecular compounds
- Deep learning methods for design of molecules with desired properties