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 presents an effective approach to quantize the electromagnetic response of arbitrarily shaped metallic nanoparticles with realistic, frequency-dependent dielectric functions, enabling accurate modeling of plexcitonic systems by bridging classical macroscopic polarization with quantum-chemical molecular descriptions.
This paper argues that the Third Law of thermodynamics is logically redundant because the Second Law already precludes the cycles at absolute zero that Einstein claimed necessitated an independent postulate, thereby positioning the Nernst heat theorem as a consistency regulator rather than a distinct physical discovery.
This paper establishes a theoretical and numerical framework for density-potential inversion in periodic systems using Moreau-Yosida-regularized density-functional theory, leveraging lower semicontinuity of the kinetic-energy functional and proximal mappings to recover exchange-correlation potentials for Kohn-Sham and Gross-Pitaevskii equations.
This paper introduces a nonperturbative variational framework based on a state-dependent polaron transformation and second-order corrections that accurately models ground states across weak, intermediate, and ultrastrong coupling regimes for both light-matter and electron-phonon systems, achieving high precision in benchmark tests like the Dicke and Holstein models.
This study demonstrates that DFT-trained MACE neural network potentials accurately reproduce the structural, dynamic, and kinetic properties of aqueous magnesium solutions, including water-exchange mechanisms, but currently fail to quantitatively capture solvation free energies due to limitations in modeling long-range electrostatic effects.
This study presents a refined linearized augmented plane wave (LAPW) method that consistently constructs radial basis functions and core orbitals with the Hartree-Fock Hamiltonian to achieve micro-Hartree precision in total energies, thereby providing rigorous all-electron benchmarks for semiconductors and insulators while validating the practical accuracy of standard semi-local approaches for relative energies.
This paper identifies a significant flaw in the existing WTMAD-2 weighting scheme for the GMTKN55 benchmark set that underweights certain components, and proposes a new WTMAD-4 metric based on typical errors of dispersion-corrected functionals to ensure fair evaluation across all benchmarks, which subsequently reveals performance issues in functionals previously optimized using the flawed metric.
This paper introduces a machine learning framework utilizing a Gaussian mixture model to extract vibronic couplings and extrapolate 2DES spectra from limited or noisy data, thereby optimizing experimental design and maximizing insights across diverse molecular systems with minimal cost.
This paper establishes a theoretical framework based on a living chain model that quantitatively links reversible hydrogen bonding dynamics to the supramolecular structure, four distinct relaxation timescales, and macroscopic dielectric and viscoelastic properties of monohydroxy alcohols across five identified dynamic regimes.
This paper explains the phenomenon of grokking in deep neural networks as a noise-driven escape from metastable states during first-order phase transitions induced by L2 regularization, where stochastic gradient descent noise eventually allows the model to overcome energy barriers and achieve generalization after prolonged overfitting.