1032 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 OrbEvo, an equivariant graph transformer model that efficiently predicts time-dependent electronic wavefunctions and related physical properties under external fields by learning to evolve orbital coefficients through autoregressive rollout, thereby overcoming the computational bottlenecks of conventional real-time time-dependent density functional theory.
This paper presents and experimentally demonstrates two independent optical methods for absolute primary nanothermometry using rare-earth-doped nanoparticles, which determine temperature solely from the internal population dynamics of Stark sublevels without external references, thereby enabling single-ion, wide-range thermal sensing at the nanoscale.
This paper introduces an optimal-tuning scheme for multiconfigurational short-range density functional theory (MC-srDFT) that determines the system-specific range-separation parameter via the ionization potential derived from Extended Koopmans' Theorem, significantly improving the accuracy of static and dynamic dipole polarizabilities compared to standard universal parameters.
This study employs Population Annealing with an adaptive temperature schedule and structure-resolved analysis to robustly map the thermodynamic landscape and quantify free energy differences between competing structural basins in the 38-atom Lennard-Jones cluster.
This paper demonstrates that short-ranged machine learned interatomic potentials (MLIPs) fail to capture long-ranged electrostatic interactions, leading to unphysical "false metallization" in polar solvents like water, a flaw that is resolved only by explicitly including long-range electrostatics.
This paper introduces a nonparametric framework that optimizes reaction coordinates by incorporating trajectory histories to overcome standard machine learning limitations, enabling robust characterization of rare event dynamics in complex systems like protein folding and climate models without requiring extensive sampling or ground truth data.
This paper presents a phase-sensitive tip-enhanced sum frequency generation spectroscopy technique that utilizes temporally asymmetric pulses to suppress non-resonant background interference, thereby achieving nanoscale spatial resolution and enabling the detection of weak vibrational signals and determination of absolute molecular orientations at surfaces.
This study employs deep learning with atom-centered symmetry functions and explainable AI to identify accurate reaction coordinates for NaCl ion pair dissociation in water and interpret the underlying solvent effects through the analysis of water bridging structures.
The paper introduces MAD-1.5, a highly curated and standardized dataset covering 102 elements with consistent r²SCAN DFT calculations, which enables the development of the PET-MAD-1.5 model achieving exceptional accuracy and stability for universal atomistic machine learning.
This paper introduces GET-SEI, a general framework combining graph contrastive learning, extended dynamic mode decomposition, and transition path theory to automatically characterize local atomic environments and quantify lithium transport kinetics and pathways across diverse solid-state electrolyte/lithium metal interfaces for targeted SEI engineering.