Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries

TL;DR

This paper introduces a multiscale computational framework that bridges atomistic simulations and device-scale modeling for electrode materials, demonstrated on sodium manganese hexacyanoferrate for sodium-ion batteries, enabling accurate property prediction and performance simulation.

Contribution

The study develops a generalizable, machine learning-based multiscale modeling approach that links atomistic simulations to electrode-scale predictions for insertion-type materials.

Findings

Accurately reproduces experimental properties of the cathode material.

Reveals a four-order-of-magnitude difference in sodium diffusivity between phases.

Predicts phase-boundary propagation and rate performance at the electrode scale.

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.