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Upscaling from Ab Initio Atomistic Simulations to Electrode Scale: The Case of Manganese Hexacyanoferrate, a Cathode Material for Na-Ion Batteries

Yuan-Chi Yang1, Eric Woillez1, Quentin Jacquet2, and Ambroise Van Roekeghem1,*

  • *Contact author: ambroise.vanroekeghem@cea.fr
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PRX Energy 5, 033001 – Published 1 July, 2026

DOI: https://doi.org/10.1103/ydcg-9cy4

Abstract

We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a moment tensor potential through iterative hybrid grand-canonical Monte Carlo-molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties, including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters—temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes—to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.

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Popular Summary

Developing high-performance batteries often requires years of experimental trial and error. To address this bottleneck, scientists have introduced a simulation blueprint that creates multiscale models capable of predicting physical properties from the atomic scale to the device scale. This study focuses on manganese hexacyanoferrate, a low-cost and sustainable material for sodium-ion batteries. The authors trained a highly accurate machine-learning model capable of simulating atomic properties with quantum mechanical accuracy. The model successfully predicts the coexistence of sodium-poor and sodium-rich phases during operation and reveals that sodium ions move ten thousand times faster in the former (sodium-poor) phase than in the latter, a crucial kinetic property. By feeding these atomic-scale discoveries into a device-scale model, researchers can now predict battery charging speeds and overall performance. This framework provides a blueprint for the rapid, simulation-led design of next-generation material systems involving the insertion and removal of atoms, such as battery and gas storage materials.

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Outline

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PRX Energy 5, 033001– Published 1 July, 2026
  • Received 31 December 2025
  • Revised 28 April 2026
  • Accepted 27 May 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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