Software / AI

Different Simulation Techniques for Materials Modeling Simulation Type

Tools

Technique

Best Use Case

Neural Network Potential NNP - Molecular dynamics MD

DeepMDKit, LAMMPS, GAP

ML-trained potentials for atomistic dynamics

Li/LLZO interface modeling with near accuracy

Kinetic Monte Carlo + Continuum modeling

Kmos, Zacros, MOOSE

Stochastic rare-event + PDE-based mesoscale modeling

SEI growth, electrolyte reduction over long timescales

3D GAN Microstructure Generation

PyTorch, TensorFlow

Deep generative models for realistic electrode structures

To predict tortuosity & porosity for Li-ion transport

Data-Driven High-Throughput

Materials Project, AiiDA ASE

High-throughput ML + physics workflows

Rapid discovery of solid-state electrolytes with high Li-ion mobility

Available Tools

From Atoms to Interfaces: ML and multiscale modeling accelerate battery breakthroughs, predicting ion transport, optimizing interactions, and enabling data-driven design Source: Rinon Iwasaki, Naoto Tanibata, Hayami Takeda & Masanobu Nakayama, Communications Materials, 2024; Kwangnam Kim, Aniruddha Dive, Andrew Grieder, Nicole Adelstein, ShinYoung Kang, Liwen F. Wan, Brandon C. Wood, Journal of Chemical Physics, 2022; Ravi N, Methekar, Paul W.C., Northrop et.al., Journal of The Electrochemical Society, 2011; Peter J. Weddle, Evan Walter Clark Spotte-Smith et.al., Electrochimica Acta, 2023;, Donal P. Finegan et.al., Nature Communications, 2020; Bing He, Shuting Chi, Anjiang Ye, et.al., Scientific data, 2020

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