ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs

• URL: http://arxiv.org/abs/2509.24115v1
• Date: Sun, 28 Sep 2025 23:18:57 GMT
• Title: ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
• Authors: Evan Dramko, Yihuang Xiong, Yizhi Zhu, Geoffroy Hautier, Thomas Reps, Christopher Jermaine, Anastasios Kyrillidis,
• Abstract summary: First-principles methods are widely used to compute defect energetics and structures, including at scale for defect databases.<n>MLFFs are computationally expensive, making machine-learning force fields (MLFFs) an attractive alternative for accelerating structural relaxations.<n>Most existing MLFFs are based on graph neural networks (GNNs), which can suffer from oversmoothing and poor representation of long-range interactions.<n>We introduce the Accelerated Deep Atomic Potential Transformer (ADAPT), an MLFF that replaces graph representations with a direct coordinates-in-space formulation.
• Score: 6.683253616106448
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 a roughly 33 percent reduction in both force and energy prediction errors relative to a state-of-the-art GNN-based model, while requiring only a fraction of the computational cost.

Related papers

• FEM-Informed Hypergraph Neural Networks for Efficient Elastoplasticity [3.211924713637672]
  Graph neural networks (GNNs) naturally align with sparse operators and unstructured discretizations.<n>Motivated by discrete physics losses, we embed finite-element computations at nodes and Gauss points directly into message-passing layers.<n>We propose a numerically consistent FEM-Informed Hypergraph Neural Networks (FHGNN)
  arXiv  Detail & Related papers  (2026-02-07T05:11:12Z)
• Facet: highly efficient E(3)-equivariant networks for interatomic potentials [6.741915610607818]
  Computational materials discovery is limited by the high cost of first-principles calculations.<n>Machine learning potentials that predict energies from crystal structures are promising, but existing methods face computational bottlenecks.<n>We present Facet, a GNN architecture for efficient ML potentials.
  arXiv  Detail & Related papers  (2025-09-10T09:06:24Z)
• Enabling Automatic Differentiation with Mollified Graph Neural Operators [75.3183193262225]
  We propose the mollified graph neural operator (mGNO), the first method to leverage automatic differentiation and compute emphexact gradients on arbitrary geometries.<n>For a PDE example on regular grids, mGNO paired with autograd reduced the L2 relative data error by 20x compared to finite differences.<n>It can also solve PDEs on unstructured point clouds seamlessly, using physics losses only, at resolutions vastly lower than those needed for finite differences to be accurate enough.
  arXiv  Detail & Related papers  (2025-04-11T06:16:30Z)
• Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection [1.474723404975345]
  Machine learning-based embedded systems for safety-critical applications must be robust to perturbations caused by soft errors.<n>We focus on encoder-decoder convolutional models developed for semantic segmentation of hyperspectral images with application to autonomous driving systems.
  arXiv  Detail & Related papers  (2025-04-07T14:21:31Z)
• CWF: Consolidating Weak Features in High-quality Mesh Simplification [50.634070540791555]
  We propose a smooth functional that simultaneously considers all of these requirements. The functional comprises a normal anisotropy term and a Centroidal Voronoi Tessellation (CVT) energy term.
  arXiv  Detail & Related papers  (2024-04-24T05:37:17Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Learning Physical Dynamics with Subequivariant Graph Neural Networks [99.41677381754678]
  Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization. Our model achieves on average over 3% enhancement in contact prediction accuracy across 8 scenarios on Physion and 2X lower rollout MSE on RigidFall.
  arXiv  Detail & Related papers  (2022-10-13T10:00:30Z)
• Transformer with Implicit Edges for Particle-based Physics Simulation [135.77656965678196]
  Transformer with Implicit Edges (TIE) captures the rich semantics of particle interactions in an edge-free manner. We evaluate our model on diverse domains of varying complexity and materials.
  arXiv  Detail & Related papers  (2022-07-22T03:45:29Z)
• Rotation Invariant Graph Neural Networks using Spin Convolutions [28.4962005849904]
  Machine learning approaches have the potential to approximate Density Functional Theory (DFT) in a computationally efficient manner. We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network. Results are demonstrated on the large-scale Open Catalyst 2020 dataset.
  arXiv  Detail & Related papers  (2021-06-17T14:59:34Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)
• Physics informed deep learning for computational elastodynamics without labeled data [13.084113582897965]
  We present a physics-informed neural network (PINN) with mixed-variable output to model elastodynamics problems without resort to labeled data. Results show the promise of PINN in the context of computational mechanics applications.
  arXiv  Detail & Related papers  (2020-06-10T19:05:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.