arXiv:2603. 14700v2 Announce Type: replace-cross Abstract: Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations.
By William J. Baldwin, Ilyes Batatia, Martin Vondr\'ak, Johannes T. Margraf, G\'abor Cs\'anyi
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.
By Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano, Matthias F. Kling, Thomas Linker
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.
By Yoonjae Park, Dongjin Kim, Daniel S. King, Nam H. {\DJ}\`ao, Roya Savoj, Sebastien Hamel, Xiaoyu Wang, Bingqing Cheng
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.
By Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan, Henrik H. Kristoffersen, August E. G. Mikkelsen, Xueping Qin, Xin Yang, Heine A. Hansen, Arghya Bhowmik, Tejs Vegge
arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.
By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both...