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 introduces a machine learning model, condensed into an analytical expression, that predicts the electric dipole moments of diatomic molecules using only atomic properties to screen the periodic table for molecules with the largest dipole moments and to reveal underlying chemical trends.
This paper demonstrates that nonlinear optical properties of individual quantum emitters, such as Raman features, can manifest in the linear spectra of coupled emitter arrays through inter-emitter interactions, revealing a general quantum optical effect that transcends classical mean-field descriptions and does not require cavities or specific symmetries.
This study demonstrates that action-detected two-dimensional electronic spectroscopy (A-2DES) can effectively probe energy transfer dynamics in cyanobacterial photosynthetic proteins, overcoming previous limitations by revealing that slow exciton annihilation modifies the expected 1/N sensitivity scaling, thereby validating A-2DES as a robust tool for investigating exciton diffusion in large aggregates.
This paper proposes a novel method for solving the time-dependent Schrödinger equation by modeling Bohmian trajectories as a self-consistent normalizing flow, where a neural network learns the score function to recover quantum dynamics for nodeless wave functions.
This conceptual work establishes a unified derivative hierarchy linking force fields and density functional theory by demonstrating how the Born-Oppenheimer energy surface, forces, and nuclear Hessians arise from pulling back the energy functional and its density-based response derivatives from external potential space to nuclear configuration space.
This study demonstrates that Born-Oppenheimer molecular dynamics based on density functional-based tight-binding accurately captures low-frequency spectral density features arising from both slow intramolecular vibrations and protein fluctuations in bacteriochlorophyll molecules, outperforming classical force fields and normal mode analysis across various light-harvesting complexes.
This paper investigates an accelerated surface hopping scheme for simulating ultrafast nonadiabatic processes by scaling spin-orbit couplings, demonstrating that while machine learning models can accurately predict potential energy surfaces and couplings to reduce computational costs, the final extrapolated time constants remain highly sensitive to fitting parameters, highlighting both the potential and current limitations of ML-enhanced reliability in this approach.
This review critically assesses the state-of-the-art in AI-powered surrogate modeling for multiscale combustion, comparing various learning approaches across scales from chemical kinetics to engine systems while highlighting key challenges like transferability and extrapolation errors, and identifying future opportunities for developing reliable, physically grounded frameworks.
This paper uses molecular dynamics simulations and quantum rate theories to demonstrate that triplet yields in organic photosensitizers are highly sensitive to the dielectric stabilization of charge-transfer intermediates provided by the surrounding solvent.
Computer simulations of 2D binary alcohol mixtures reveal that, contrary to 3D behavior, mixtures of short and long chains remain well-mixed due to a complex interplay between charge ordering and micro-phase separation that defies conventional fluctuation-based explanations.