Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models
- 1Ilmenau University of Technology, Theoretical Solid State Physics, Weimarer Straße 32, 98693 Ilmenau, Germany
- 2Martin-Luther-University Halle-Wittenberg, Institute of Chemistry, Theoretical Chemistry, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
Abstract
High Resolution Image Download MS PowerPoint Slide Lithium diffusion in silicon battery anodes is governed by thermally activated jumps between (meta)stable sites separated by significant energy barriers, making such events rare on ab initio molecular dynamics (AIMD) time scales. To overcome this limitation, we establish a multiscale workflow that links AIMD, machine-learned force fields (MLFFs), and Markov state models (MSMs) to bridge atomistic mechanisms to mesoscale diffusion. Focusing on crystalline Li–Si phases, our MLFFs trained on AIMD data, achieve near-DFT accuracy while enabling large-scale molecular dynamics simulations extending to tens of nanoseconds. From these trajectories, we extract converged lithium-jump statistics to construct MSMs that quantitatively reproduce diffusivities with uncertainties an order of magnitude smaller than those obtained from 100 ps AIMD simulations. Demonstrated here for crystalline Li x Si y phases, the AIMD → MLFF → MSM workflow provides a transferable route for quantitative transport modeling in amorphous structures, defect-mediated diffusion, and alternative solid-state anodes.
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BibTeX
@article{qaisrani2026bridging,
title = {{Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models}},
author = {Muhammad Nawaz Qaisrani and Christoph Kirsch and Aaron Flötotto and Jonas Hänseroth and Jules Oumard and Daniel Sebastiani and Christian Dreßler},
journal = {Journal of Chemical Theory and Computation},
year = {2026},
month = may,
doi = {10.1021/acs.jctc.5c02035},
url = {https://doi.org/10.1021/acs.jctc.5c02035},
note = {CC BY},
}
RIS
TY - JOUR
TI - Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models
AU - Muhammad Nawaz Qaisrani
AU - Christoph Kirsch
AU - Aaron Flötotto
AU - Jonas Hänseroth
AU - Jules Oumard
AU - Daniel Sebastiani
AU - Christian Dreßler
PY - 2026
DA - 2026/05/20
JO - Journal of Chemical Theory and Computation
DO - 10.1021/acs.jctc.5c02035
UR - https://doi.org/10.1021/acs.jctc.5c02035
AB - High Resolution Image Download MS PowerPoint Slide Lithium diffusion in silicon battery anodes is governed by thermally activated jumps between (meta)stable sites separated by significant energy barriers, making such events rare on ab initio molecular dynamics (AIMD) time scales. To overcome this limitation, we establish a multiscale workflow that links AIMD, machine-learned force fields (MLFFs), and Markov state models (MSMs) to bridge atomistic mechanisms to mesoscale diffusion. Focusing on crystalline Li–Si phases, our MLFFs trained on AIMD data, achieve near-DFT accuracy while enabling large-scale molecular dynamics simulations extending to tens of nanoseconds. From these trajectories, we extract converged lithium-jump statistics to construct MSMs that quantitatively reproduce diffusivities with uncertainties an order of magnitude smaller than those obtained from 100 ps AIMD simulations. Demonstrated here for crystalline Li x Si y phases, the AIMD → MLFF → MSM workflow provides a transferable route for quantitative transport modeling in amorphous structures, defect-mediated diffusion, and alternative solid-state anodes.
SN - 1549-9618
SN - 1549-9626
ER -