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: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:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.
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.
The paper introduces Orbformer, a transferable wavefunction model that uses deep neural networks to pretrain on 22,000 equilibrium and dissociating molecular structures. Fine‑tuning Orbformer on unseen molecules achieves an accuracy‑cost ratio comparable to classical multireference methods, consistently reaching chemical accuracy (1 kcal/mol) on standard benchmarks, challenging bond dissociations, and Diels‑Alder reactions. This demonstrates that amortizing the cost of solving the Schrödinger equation across many molecules is feasible in quantum chemistry.
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.
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: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. 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: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.
The paper introduces Gaussian Splatting for Density Functional Theory (GS‑DFT), a method that represents molecular orbitals as a cloud of Gaussians optimized via gradient descent. GS‑DFT replaces fixed atom‑centered basis sets with an adaptive, differentiable orthogonalization and efficient two‑electron integral evaluation, achieving accuracy comparable to large conventional bases with far fewer parameters. The solver scales quadratically with cloud size, enabling simulations of up to 2,742 atoms on a single four‑GPU node at triple‑zeta precision.