CROSS-REFERENCE TO RELATED APPLICATIONS

None.

FIELD

The present disclosure relates to methods, apparatuses, and systems for battery material screening and, more particularly, to battery material screening using a data-driven high-throughput screening framework.

BACKGROUND

Rechargeable battery material development has been on the forefront of scientific and commercial research due to the popularity of electric vehicles. However, the nearly infinite number of possible material combinations for each battery component (e.g., cathode, anode, electrolyte, and separator) makes for an unworkable problem for today's researchers. In particular, identification of optimal material combinations require numerous candidates to be slowly evaluated by physical experiments and/or computer simulations. Advances in creating synthetic materials via computer simulations has decreased such effort, but one still needs to focus on a reduced selection of material combinations.

Physical experiments make up the bulk of these intensive efforts in terms of machine and material cost, manual labor, and relatively long execution time. However, simulation environments enable carrying out virtual experiments at less cost and faster speed. These virtual experiments are calibrated from representative real experiments and can thus approximate the experimental behavior considering simplified physical knowledge.

Unfortunately, even with advances in real world experiments and computer simulations backed by billions of dollars of funding, such state of the art design processes are inadequate to fully explore the wide assortment of possible permutations for battery materials. Furthermore, even with sophisticated computer simulations, the compute needed to carry out such simulations can take several minutes to hours, depending on the complexity of the material structure. This would not be feasible when considering the number of permutations of possible candidates.

Therefore, new methods, apparatuses, and systems for battery material screening are desired to speed up development process by using data-driven artificial intelligence (AI) models.

