The paper introduces eSpinor, an SU(2)-equivariant graph neural network that propagates spinor features for predicting molecular magnetism. By initializing spinor features from eigenvectors of an approximate spin‑orbit coupling Hamiltonian and employing gauge‑covariant message passing with a gauge‑invariant readout, eSpinor achieves more accurate zero‑field‑splitting predictions for cobalt single‑molecule magnets and improves electron‑spin correlation predictions for a dysprosium MRI probe compared to existing methods. The study demonstrates that incorporating SU(2) symmetry as an inductive bias enhances performance on magnetic properties and highlights that static tensor errors can lead to different dynamical errors, suggesting careful model selection for specific observables.
By Erh-Wei Sheng, Beom Seok Kang, Yuchao Lin, Danish Khan, Anima Anandkumar
CrystalREPA is a plug‑and‑play framework that aligns the atom‑wise hidden states of crystal generative encoders with frozen universal machine‑learning interatomic potential (MLIP) representations using an element‑aware contrastive objective. By doing so, it transfers stability‑aware atomistic priors to generative models with minimal training overhead and no extra inference cost, consistently improving thermodynamic stability, structural validity, and fidelity across multiple generative frameworks and benchmark datasets. The study also shows that an MLIP’s suitability as a teacher is better predicted by the distinguishability of its atom‑wise representation space than by its leaderboard accuracy, providing a practical, accuracy‑independent selection criterion.
whyItMatters":"The approach demonstrates a practical method to enhance crystal generative models’ ability to produce stable, realistic structures by leveraging existing MLIP knowledge without incurring additional inference costs."
By Chengqian Zhang, Yucheng Jin, Duo Zhang, Tiejun Li, Han Wang
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:2509. 21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy.
By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik
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:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.
By Irina Espejo Morales, Damon Hinz, Marvin Alberts, Geraud Krawezik, Haewon Jeong, Shirley Ho
arXiv:2607. 28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions.
By Ali Rayat, Yunhao Fan, Gia-Wei Chern
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
arXiv:2606. 22984v2 Announce Type: replace-cross Abstract: Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization.
By Lu Zhong, Wenli Duan, Jing Liu, Pan Zhang, Ying Tang
The paper presents a multitask learning approach that incorporates orbitally resolved semiempirical charges into machine learning interatomic potentials (MLIPs). By training with inexpensive GFN1‑xTB orbital charges, the model reduces energy mean absolute error by 58% compared to energy‑only baselines and needs five times less data to achieve similar performance. The method also outperforms or matches models using costly DFT charges and preserves inference efficiency, offering a scalable way to build accurate, data‑efficient foundation models for complex chemical systems.
By Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr Voznyy
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
arXiv:2606. 16575v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable success in scientific computing, yet they often suffer from spectral bias in capturing oscillatory and multiscale behaviors.
By Yong Wang, Tao Zhou, Xuhui Meng