Sources and links on page 458 (15)
Links embedded in the original report PDF, including citations and other references. These destinations have not been independently reviewed. Links open in a new tab.
- https://www.nature.com/articles/s43246-024-00595-0
- https://pubs.aip.org/aip/jcp/article-abstract/156/22/221101/2841294/Flexible-machine-learning-interatomic-potential?redirectedFrom=fulltext
- https://web.mit.edu/braatzgroup/Kinetic_Monte_Carlo_simulation_of_surface_heterogeneity_in_graphite_electrodes_for_lithium_ion_batteries_Passive_layer_formation_and_simulation_of_capacity_fade.pdf
- https://espottesmith.github.io/files/papers/weddle_continuum_level_2023.pdf
- https://www.nature.com/articles/s41467-020-15811-x
- https://www.nature.com/articles/s41597-020-0474-y
- https://github.com/deepmodeling/deepmd-kit
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- https://zacros.org/
- https://mhoffman.github.io/kmos/
- https://mooseframework.inl.gov/
- https://pytorch.org/
- https://www.tensorflow.org/
- https://next-gen.materialsproject.org/
- https://www.aiida.net/
Text on page 458
Different Simulation Techniques for Materials Modeling Simulation Type
Tools
Technique
Best Use Case
Neural Network Potential (NNP) - Molecular dynamics (MD)
DeepMD-Kit, 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
Extracted from the PDF; charts and images may read imperfectly.
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