arXiv:2603. 14700v2 Announce Type: replace-cross Abstract: Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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: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.
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.
The paper investigates whether machine learning models can uncover physical patterns in atomistic data without relying on traditional physics-based inductive biases such as geometric locality or graph structures. By training a general-purpose architecture on molecular simulation data, the authors demonstrate that the model autonomously learns interatomic interaction strengths resembling classical electrostatics and identifies interaction cutoffs aligned with established physical models. The study also reports predictable neural scaling behavior and competitive accuracy on certain metrics compared to physics-informed architectures, suggesting that explicit priors may only be necessary when empirically justified.
arXiv:2609.39090v1 Announce Type: cross Abstract: Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of...