The paper introduces a neural network architecture that learns a latent, implicit basis representation of the electronic-state Hamiltonian, enabling a unified treatment of ground and excited states, conical intersections, and non‑adiabatic couplings. The model is trained on realistic photochemical systems—thymine and azobenzene—and accurately reproduces energies, oscillator strengths, and critical geometries such as conical intersections and excited‑state minima. It also captures Berry phase accumulation around conical intersections and can be extended to learn other operators like transition dipole moments.
arXiv:2503. 19847v2 Announce Type: replace-cross Abstract: The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods.
arXiv:2609.37733v1 Announce Type: cross Abstract: Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a s...
arXiv:2606. 14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve.
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:2607. 17033v1 Announce Type: new Abstract: Forecasting the outcomes of transition-metal-catalyzed reactions is notoriously complex due to the interplay of diverse physical and chemical variables.
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: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:2607. 13737v1 Announce Type: new Abstract: For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective.
arXiv:2608. 14076v1 Announce Type: cross Abstract: Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations.
arXiv:2609.14906v1 Announce Type: cross Abstract: The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system...
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.
The paper introduces SO(3) Equivariant Neural Kalman Networks (SENK), a model that predicts vibrational spectra by cascading an equivariant transformer backbone, an Equivariant Neural Kalman bridge, and an NBO-informed electronic-prior pathway. SENK improves accuracy over DetaNet on QM9S and QMe14S datasets while maintaining full-spectrum IR and Raman fidelity from small molecules to drug-like systems. It also remains stable and selectively enhances spectrally sensitive features in biomolecular systems with complex stereoelectronic effects, enabling transferable vibrational spectroscopy across diverse molecular materials.