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.
arXiv:2607. 18332v1 Announce Type: cross Abstract: Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery.
The paper investigates how small entry‑wise errors in machine‑learned Kohn‑Sham Hamiltonians can still lead to inaccurate eigenvalues and eigenvectors. It shows that existing physics‑informed losses only partially capture the error, and proposes using the cumulative local density of states (cumulative LDOS) as a complete training signal that uniquely determines the Hamiltonian. Fine‑tuning three Hamiltonian networks with this loss, called FermiScope, reduces HOMO‑LUMO gap errors without increasing element‑wise error, and the improvement generalizes to out‑of‑distribution and molecular‑dynamics scenarios.
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.
arXiv:2606. 10349v1 Announce Type: cross Abstract: We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disordered itinerant magnets.
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.
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...
arXiv:2610.18924v1 Announce Type: cross Abstract: Interpreting electron paramagnetic resonance (EPR) spectra commonly requires repeated forward simulation and optimization of spin Hamiltonian paramet...
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.
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
arXiv:2607. 28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions.
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.
arXiv:2606. 18691v1 Announce Type: new Abstract: Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces.