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Accelerated Deep Atomic Potential Transformer for long-range machine learning force fields without graphs

Evan Dramko1, Yizhi Zhu2,3,4, Yihuang Xiong4, Geoffroy Hautier2,3,4,*, Thomas Reps5, Christopher Jermaine1, and Anastasios Kyrillidis1

  • *Contact author: geoffroy.hautier@rice.edu
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Phys. Rev. Research 8, 033266 – Published 3 September, 2026

DOI: https://doi.org/10.1103/3xf3-ts5f

Abstract

Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale for high-throughput defect databases. However, these methods are computationally expensive, making machine learning force fields (MLFFs) an attractive alternative for accelerating structural relaxations. Most existing MLFFs are based on graph neural networks (GNNs), which can suffer from oversmoothing, oversquashing, and poor representation of long-range interactions. Both of these issues are especially of concern when modeling point defects. To address these challenges, we introduce the Accelerated Deep Atomic Potential Transformer (ADAPT), an MLFF that replaces graph representations with a direct coordinates-in-space formulation and explicitly considers all pairwise atomic interactions. Atoms are treated as “tokens,” with a transformer encoder modeling their interactions. Applied to a dataset of silicon point defects, ADAPT achieves an ∼22% reduction in force and an ∼40 reduction in energy prediction error relative to a state-of-the-art GNN-based model, while requiring only a fraction of the computational cost.

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Phys. Rev. Research 8, 033266– Published 3 September, 2026
  • Received 2 March 2026
  • Accepted 21 July 2026
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