mlsanityData to 14 Oct 2026

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration

arXiv 2501.19179 ↗First posted 31 Jan 2025physics.chem-ph cs.LG physics.comp-phcited by 5

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Abstract (arXiv)

Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local. However, locality precludes modeling long-range effects critical to many real-world systems, such as charge transfer, electrostatic interactions, and dispersion effects. In this work, we propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenge of efficiently modeling non-local interactions. This novel architecture generalizes the classical charge equilibration (Qeq) method to a model-agnostic building block for modern equivariant GNN potentials. Therefore, CELLI extends the capability of GNNs to model long-range interactions while providing high interpretability through explicitly modeled charges. On benchmark systems, CELLI achieves state-of-the-art results for strictly local models. CELLI generalizes to diverse datasets and large structures while providing high computational efficiency and robust predictions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 66 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

23 of 66 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  4. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  5. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  6. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  7. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 255
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  9. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  10. 2022The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialscited by 101
  11. 2022SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentialscited by 82
  12. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
11 more
  1. 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
  2. 2017Machine Learning Molecular Dynamics for the Simulation of Infrared Spectracited by 30
  3. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
  4. 2020Machine learning of solvent effects on molecular spectra and reactionscited by 14
  5. 2022Capturing long-range interaction with reciprocal space neural networkcited by 13
  6. 2023Accurate machine learning force fields via experimental and simulation data fusioncited by 12
  7. 2024chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physicscited by 12
  8. 2024Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Costcited by 12
  9. 2024Predicting solvation free energies with an implicit solvent machine learning potentialcited by 10
  10. 2024Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNscited by 9
  11. 2024Learning charges and long-range interactions from energies and forcescited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationscited by 6
  2. 2025Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spacescited by 2
  3. 2026Polarizable atomic multipoles for learning long-range electrostaticscited by 2
  4. 2026ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potentialcited by 1
  5. 2025Long-range electrostatics for machine learning interatomic potentials is easier than we thoughtcited by 0