mlsanityData to 14 Oct 2026

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence

arXiv 2507.19438 ↗First posted 25 Jul 2025cond-mat.mtrl-sci cs.LGcited by 1

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Abstract (arXiv)

Machine learning interatomic potentials have become an indispensable tool for materials science, enabling the study of larger systems and longer timescales. State-of-the-art models are generally graph neural networks that employ message passing to iteratively update atomic embeddings that are ultimately used for predicting properties. In this work we extend the message passing formalism with the inclusion of a continuous variable that accounts for fractional atomic existence. This allows us to calculate the gradient of the Gibbs free energy with respect to both the Cartesian coordinates of atoms and their existence. Using this we propose a gradient-based grand canonical optimization method and document its capabilities for a Cu(110) surface oxide.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 96 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

21 of 96 references.

References · 21

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  5. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  6. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  7. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 571
  8. 2020Directional Message Passing for Molecular Graphscited by 561
  9. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  10. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
  11. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  12. 2019Cormorant: Covariant Molecular Neural Networkscited by 263
9 more
  1. 2025Learning Smooth and Expressive Interatomic Potentials for Physical Property Predictioncited by 84
  2. 2017Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Spacecited by 76
  3. 2024Orb: A Fast, Scalable Neural Network Potentialcited by 66
  4. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  5. 2022Data-driven discovery of novel 2D materials by deep generative modelscited by 18
  6. 2022Hyperactive Learning (HAL) for Data-Driven Interatomic Potentialscited by 17
  7. 2023Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialscited by 16
  8. 2021Generating stable molecules using imitation and reinforcement learningcited by 9
  9. 2024Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentialscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Grand Canonical Generatorscited by 0