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...
By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik
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.
By Bumju Kwak, Jeonghee Jo
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.
By Beom Seok Kang, Vignesh C. Bhethanabotla, Amin Tavakoli, Maurice D. Hanisch, Arimitsu Horikawa-Strakovsky, Miguel Nouman, Danish Khan, William A. Goddard III, Anima Anandkumar
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik
arXiv:2607. 29158v1 Announce Type: cross Abstract: We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations.
By Johannes Mae{\ss}, Leon Werner, J. Thorben Frank, Winfried Ripken, Martin Michajlow, Joshua Futterer, Klaus-Robert M\"uller, Stefan Chmiela
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.
By G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, A. Melnikov