Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials
arXiv:2606.15892v1 Announce Type: new Abstract: 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: l
The rapid advancement in AI and computational materials science has created an urgent need for more accurate and efficient simulation methods, driving innovation in interatomic potentials.
Improved interatomic potentials can accelerate materials discovery and drug design by enabling larger and longer-duration molecular dynamics simulations with higher fidelity, reducing the reliance on computationally expensive quantum mechanical methods.
This research suggests a pathway to more physically accurate and deployable energy surfaces for interatomic potentials, potentially unlocking new capabilities in molecular dynamics simulations for various scientific and engineering applications.
- · Materials Science Researchers
- · Pharmaceutical Companies
- · Chemical Engineering Firms
- · AI/ML for Science Developers
- · Traditional high-cost simulation methods
- · Inefficient materials R&D processes
Molecular dynamics simulations will become significantly more accurate and accessible for complex systems.
This enhanced simulation capability will accelerate the discovery and optimization of new materials and drug candidates.
Reduced R&D timelines could lead to faster market entry for innovative products across diverse industries, impacting global competitiveness.
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Read at arXiv cs.LG