Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials
Not on the map. Why?
Abstract (arXiv)
Accurate interatomic potentials enable molecular dynamics of materials, molecules, and interfaces beyond density-functional-theory length and time scales. Equivariant neural network potentials have improved the representation of local geometry. However, their deployable energy surfaces ultimately manifest through invariant scalar channels, whose aggregation and spectral resolution remain comparatively underexamined. Here we use Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixers as controlled scalar-pathway probes: lightweight, symmetry-preserving modifications that act only on (ℓ=0) channels while leaving the equivariant tensor backbone unchanged. Using MACE as a high-body-order mechanistic scaffold, PAN adds coordination-sensitive amplitude modulation, whereas PGS augments edge and readout scalar features with radial and tapered spectral bases. Across metallic Ag, covalent Si, a short-range ionic LiF/Li--F subset, and MD17/rMD17 molecules, this scalar-pathway correction reduces MACE force errors by 22--27% and energy errors by 19--22%; on systems with stress labels, stress errors decrease by 27--28%, at approximately 5% additional inference-FLOPs cost. Directionally consistent gains in Allegro and NequIP further indicate that the correction is portable across distinct short-range equivariant backbones, although effect sizes remain architecture-dependent. These results identify scalar-pathway fidelity as a practical design dimension for short-range equivariant interatomic potentials.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 14 Jun 2026.
Builds on
24 of 72 references.
References · 24
- 2017Neural Message Passing for Quantum Chemistrycited by 3,487
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
- 2018On the Spectral Bias of Neural Networkscited by 1,341
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
- 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
- 2024KAN: Kolmogorov-Arnold Networkscited by 649
- 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 631
- 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 607
- 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 571
- 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
- 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 441
12 more
- 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 343
- 2019Cormorant: Covariant Molecular Neural Networkscited by 263
- 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
- 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
- 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 214
- 2020Machine Learning Force Fieldscited by 207
- 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 204
- 2022e3nn: Euclidean Neural Networkscited by 174
- 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
- 2022The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentialscited by 101
- 2024Orb: A Fast, Scalable Neural Network Potentialcited by 66
- 2021Directional Message Passing on Molecular Graphs via Synthetic Coordinatescited by 24
Built on by
Not cited yet.