785 papers
This collection explores the fascinating intersection where the laws of physics meet the complex machinery of chemistry. Here, researchers investigate how quantum mechanics governs molecular bonds, how light interacts with matter at the atomic scale, and how fundamental forces shape chemical reactions. It is a realm where abstract mathematical models collide with tangible substances to reveal the hidden mechanisms driving our material world.
On Gist.Science, we process every new preprint in this category directly from arXiv to make these discoveries accessible to everyone. Whether you are a seasoned expert or a curious reader, you will find both plain-language explanations and detailed technical summaries for each paper. Below are the latest contributions from the community pushing the boundaries of physical chemistry.
This paper demonstrates that training a neural network-based local density approximation functional specifically on water systems, using a differentiable Kohn-Sham solver, achieves near gold-standard accuracy with minimal training data and enables effective transfer learning to other water-related systems, thereby prioritizing system-specific precision over generalizability.
This study demonstrates that microhydration systematically stabilizes the lowest two shape resonances of thymine and extends their lifetimes through a complex interplay of hydrogen bonding, electrostatic interactions, and geometric distortions, while also highlighting the critical role of diffuse basis functions and local solvation geometry in determining resonance behavior.
This paper introduces Ranking Configuration Interaction (RCI), a novel machine learning framework that reframes determinant selection in selected configuration interaction as a pairwise ranking problem using a Transformer-based architecture, thereby significantly accelerating convergence and improving accuracy compared to existing regression and classification methods.
This paper introduces QT-Net, a rotationally augmented graph neural network evaluated via a principled out-of-distribution protocol based on SOAP descriptors, which demonstrates that inferring atomic properties like electron populations and multipoles improves downstream molecular property prediction and accurately recovers ground-truth dipole moments.
This study presents the first experimental verification of a null point in a donor-acceptor dyad, demonstrating state localization and selective charge filtering through impulsive pump-probe measurements and a generalized vibronic theory, thereby validating a design principle for advanced photovoltaic materials.
This paper introduces a linear-scaling, potential-free data-driven molecular dynamics framework (PDMD) that utilizes a ChemGNN model and a novel Gaussian-based descriptor to achieve ab initio-level accuracy in predicting energies and forces for arbitrary-sized water clusters at a fraction of the computational cost of traditional methods, supported by a new large-scale ab initio dataset.
This study leverages broadband MMW-MMW double resonance spectroscopy to significantly refine the pure rotational parameters of glycidaldehyde's ground state and identify 11 new vibrationally excited states, ultimately enabling a targeted search in the ALMA ReMoCA survey of Sgr B2(N) that yielded a non-detection and established an upper limit indicating the molecule is at least six times less abundant than oxirane in that region.
This paper proposes a knowledge distillation framework that trains a refined coarse-grained neural network potential using denoised force and energy predictions from an initial teacher model, significantly improving the accuracy and stability of force fields for complex molecular fluids like deep eutectic solvents.
This paper demonstrates that spin dynamic mean-field theory (spinDMFT) is an efficient and accurate method for simulating spectral spin diffusion and zero-quantum line shapes in static disordered solids, successfully matching experimental data for test substances where exact brute-force calculations are infeasible.
This paper generalizes the Aufbau suppressed coupled cluster formalism to accurately describe doubly excited states by introducing a specialized wave function initialization strategy that achieves high accuracy (errors ~0.15 eV) at a computational cost comparable to ground-state singles and doubles theory, significantly outperforming standard equation-of-motion methods for these challenging electronic states.