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.
By Xiaoyu Wang, Bingqing Cheng
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. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.
By Sheng Bi, Yi-Ze Wang, Jun Cheng
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.
By Jia Bi, Alin Marin Elena, Samuel Pinilla
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
BranchIP introduces a single-model framework that learns adaptive tensor product computation for equivariant machine learning interatomic potentials (MLIPs). Using a novel distillation loss, it achieves up to 2.4× speed‑up and 2.6× memory reduction across model sizes while preserving physical fidelity. The adaptive computation also offers interpretability by indicating which interactions require deeper processing and how depth correlates with chemical complexity and dynamics.
By Laura Zichi, Gil Harari, Chuin Wei Tan, Marc L. Descoteaux, Albert Zhu, Menghang Wang, Yoel Zimmermann, H. T. Kung, Boris Kozinsky