# Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches

arXiv: https://arxiv.org/abs/2605.19124 · HTML: https://mlsanity.com/paper/2605.19124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 450 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 10 of 10)

- [A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems](https://mlsanity.com/paper/2312.07511/) (2023)
- [Universally Converging Representations of Matter Across Scientific Foundation Models](https://mlsanity.com/paper/2512.03750/) (2025)
- [A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention](https://mlsanity.com/paper/2603.06567/) (2026)
- [Scaling Autoregressive Models for Lattice Thermodynamics](https://mlsanity.com/paper/2603.14695/) (2026)
- [An SO(3)-equivariant reciprocal-space neural potential for long-range interactions](https://mlsanity.com/paper/2603.18389/) (2026)
- [Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment](https://mlsanity.com/paper/2604.01169/) (2026)
- [XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures](https://mlsanity.com/paper/2604.12140/) (2026)
- [Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks](https://mlsanity.com/paper/2409.13851/) (2024)
- [Reciprocal Space Attention for Learning Long-Range Interactions](https://mlsanity.com/paper/2510.13055/) (2025)
- [Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation](https://mlsanity.com/paper/2603.23210/) (2026)

## Abstract (arXiv)

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how conventional and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate various computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.