AI-Based Nano-Scale Material Property Prediction for Li-Ion Batteries

MA Lal and A Singh and R Mzik and A Lanjan and S Srinivasan, BATTERIES- BASEL, 10, 51 (2024).

DOI: 10.3390/batteries10020051

In this work, we propose a machine learning (ML)-based technique that can learn interatomic potential parameters for various particle-particle interactions employing quantum mechanics (QM) calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to molecular dynamics simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach.

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