arXiv Machine Learning
TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch
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arXiv Machine Learning
Oct 22
SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism
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 Machine Learning
Oct 28
CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models
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."
arXiv Machine Learning
Jul 1
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
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 Machine Learning
Jun 30
Shoot from the HIP: Hessian Interatomic Potentials without derivatives
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 Machine Learning
Jul 23
OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
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 Machine Learning
Jul 23
NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem
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.