MACHINE LEARNING-DRIVEN FRAMEWORK FOR PREDICTING IONIC CONDUCTIVITY OF SOLID-STATE ELECTROLYTES

Abstract: a system and method are provided for a machine-learning drive framework for predicting ionic conductivity of solid-state electrolytes. in use, the method and/or system may include receiving, at a machine learning system, two or more molecular structures from at least one structural dataset, where the two or more molecular structures relate to ionic mobility. additionally, atomic weights are calculated for the two or more molecular structures, and the machine learning system is trained based on the two or more molecular structures, where the training relies on at least one intrinsic atomic feature and the calculated atomic weights for the two or more molecular structures. further, a bias-correction is applied for the two or more molecular structures to improve the training of the machine learning system. further, one or more molecular dynamics (md) simulations are outputted, using the machine learning system, for the two or more molecular structures.

Inventor(s): Vaidish Sumaria, Jacob Vikoren, David Sommer, Takat Rawal, Deeptanshu Prasad

CPC Classification: G16C20/70 (Machine learning, data mining or chemometrics)

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