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 study resolves discrepancies in the kinetics of the NH2 + O(3P) reaction by constructing a full-dimensional, machine-learned potential energy surface using high-level ic-MRCI calculations, which enables accurate quasi-classical trajectory simulations of thermal rate coefficients and branching ratios essential for refining nitrogen-fuel combustion models.
This study establishes a highly accurate, sub-cm⁻¹ benchmark potential energy surface for the helium-benzene complex by integrating high-level CCSD(T) and SAPT calculations with multifidelity Gaussian process regression, revealing that this new potential predicts qualitatively different low-temperature solvation behaviors compared to traditional empirical models.
This paper introduces and benchmarks a new one-parameter atomic-number-based damping function (XDM(Z)) for dispersion corrections, demonstrating that when paired with specific hybrid functionals like revPBE0 and B86bPBE0, it achieves consistent high accuracy across the comprehensive GMTKN55 molecular database and molecular crystal benchmarks.
This paper introduces a reusable geometry-optimization engine in PyBEST that interfaces with \texttt{geomeTRIC} to provide the first implementation of analytic nuclear gradients for orbital-optimized pair coupled-cluster doubles (OOpCCD), enabling robust and accurate molecular structure optimization for seniority-zero wavefunctions.
This paper presents a unified machine learning framework that combines machine-learned interatomic potentials with neural classical density functional theory to enable efficient, first-principles multiscale modeling of liquid thermodynamics and phase behavior across homogeneous and inhomogeneous systems.
This paper proposes a geometric diagnostic for quantum scrambling sensitivity by utilizing Lagrangian Descriptors within a Bohmian trajectory framework over a two-dimensional preparation space of Gaussian wavepackets, demonstrating that for the inverted harmonic oscillator, this approach yields an exponential sensitivity bound comparable to Out-of-Time-Order Correlator (OTOC) growth while circumventing the uncertainty principle's obstruction to defining independent initial position and momentum.
This paper presents new batching algorithms and a generic tensor contraction protocol for coupled-cluster singles and doubles (CCSD) calculations on NVIDIA Hopper and Grace Hopper GPUs, demonstrating that optimized implementations using CuPy and PyTorch achieve up to a 16-fold speedup over previous hybrid CPU-GPU approaches, with PyTorch showing a 20% performance advantage on H100 while both libraries perform similarly on GH200.
This review examines how molecular shape, chirality, polarity, and spatial confinement induce deformed equilibrium and polydomain states with parity-breaking deformations in paraelectric and ferroelectric nematic liquid crystals, highlighting mechanisms such as splay cancellation and the interplay between elastic and electrostatic energies.
The paper introduces olLOSC, a unified and computationally efficient orbital-free density functional approximation that corrects delocalization errors in both molecules and periodic materials by calculating curvature via orbital-free electronic linear response, thereby enabling robust predictions of total energy, charge density, and band structure without the high cost of existing methods.
This study demonstrates that the transient time correlation function (TTCF) method successfully enables the simulation of water slip flow in graphene nanochannels at experimentally accessible shear rates, yielding results consistent with equilibrium simulations and experiments where classical nonequilibrium molecular dynamics fails.