The paper presents a semi‑local framework that learns long‑range electrostatics and polarization for machine‑learning interatomic potentials using polarizable atomic multipoles. Local equivariant descriptors predict environment‑dependent monopoles, dipoles, and quadrupoles, while a non‑self‑consistent linear response captures residual charge transfer and polarization. Across multiple benchmarks and MLIP architectures, this multipole hierarchy systematically improves potential energy surface accuracy and yields physically meaningful electrical responses, including accurate Born effective charges, infrared and Raman spectra, and surface‑specific vibrational signatures.
arXiv:2603. 14700v2 Announce Type: replace-cross Abstract: Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations.
arXiv:2606. 15001v1 Announce Type: cross Abstract: Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response.
arXiv:2607. 05559v1 Announce Type: new Abstract: Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy.
The paper introduces two Hessian-based data augmentation techniques—UniAug and ModeAug—to improve machine‑learning interatomic potentials (MLIPs). These methods use simple Taylor expansions to generate augmented configurations without modifying training objectives or increasing computational overhead. Experiments on both non‑equilibrium and equilibrium datasets show that the augmentations enhance model accuracy and provide practical guidelines for specific tasks.
arXiv:2607. 10887v1 Announce Type: cross Abstract: Machine learning interatomic potentials (MLPs) have revolutionized atomistic modeling, offering the potential to replace traditional methods like Density Functional Theory (DFT).
SoLiD26 is a curated dataset of 15.4 million first‑principles atomic structures for solid‑liquid interfaces, comprising up to 576 atoms and 15 chemical elements. The data were generated from density functional theory calculations, mainly ab initio molecular dynamics, and include aqueous coinage metal interfaces, electrode‑electrolyte systems, and bulk references. Each record contains atomic species, positions, cell parameters, periodic boundary conditions, potential energy, and atomic forces, all computed with VASP using the PBE functional and D3 dispersion corrections.
arXiv:2601. 07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties.
arXiv:2507. 03853v2 Announce Type: replace Abstract: We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information.
arXiv:2602. 16908v2 Announce Type: replace-cross Abstract: Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks.
The paper presents a neural operator that learns the Kohn–Sham map, directly predicting electron density from the Kohn–Sham potential without orbital diagonalization. Using a domain‑invariant SE(3)‑equivariant Fourier neural operator trained on 8,504 molecules and solids, the model achieves quasi‑linear scaling self‑consistent field (SCF) convergence across diverse systems—including organic molecules, insulators, and metals—while reproducing Kohn–Sham DFT accuracy for densities, spectra, and structural observables. This enables large‑scale simulations, such as magnesium dislocation densities with 82,500 valence electrons, on a single GPU.
arXiv:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.
arXiv:2509.21624v4 Announce Type: replace Abstract: Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate...