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 proposes a unified machine learning framework integrating Potential-Embedded MACE and Potential-Embedded Electron Density Prediction to simultaneously and accurately simulate atomic forces and electron density distributions across arbitrary electric potentials, enabling large-scale studies of electrochemical interfaces like the Pt(111)/water system.
This paper investigates and establishes the extension procedures, stability conditions, and accuracy metrics for applying extended phase-space symplectic integration to simulate both classical electron dynamics in turbulent magnetic fields and quantum Kohn-Sham time-dependent density-functional theory, thereby paving the way for its broad application across systems with finite and infinite degrees of freedom.
This paper introduces an adaptive hybrid algorithm that integrates the Climbing Image Nudged Elastic Band (CI-NEB) method with Minimum Mode Following (MMF) to accelerate convergence to relevant transition states, demonstrating significant reductions in computational costs for high-throughput automated chemical discovery.
This paper introduces an end-to-end differentiable, JAX-based framework that optimizes a single deep-learned exchange-correlation functional for both ground-state DFT and linear-response TDDFT by enabling gradient-based training through self-consistent field equations and the Casida eigenvalue problem.
This paper introduces information entropy as a general-purpose collective variable for enhanced sampling that enables the unsupervised discovery of metastable states and reaction pathways across diverse molecular and condensed-phase systems without requiring predefined reaction coordinates.
This paper demonstrates through simulations that while resonant pump-probe schemes maximize selectivity for strongly-coupled molecules, non-resonant schemes offer a robust alternative by retaining high sensitivity to these signals while minimizing optical artifacts and interference from uncoupled intracavity molecules.
This study demonstrates that the q-sc-EOM algorithm, enhanced by scaling-reduction techniques and error-mitigation strategies, can accurately compute excited-state potential energy surfaces on near-term quantum hardware, marking a significant step toward practical quantum utility in chemical simulations.
This study evaluates various semilocal and hybrid density functionals for calculating defect formation energies in fcc metals and silicon, finding that the local density approximation performs best for metals while the LAK meta-GGA achieves outstanding accuracy for silicon, and rationalizes these trends by analyzing key semilocal ingredients to guide future functional improvements.
This study demonstrates that two-colour (-) coherent control using seeded FERMI free-electron laser pulses can selectively steer the coupled electron-nuclear dynamics in molecular hydrogen photoionization, revealing how relative phases between ionization pathways depend on final vibrational states and are influenced by autoionizing resonances.
This study benchmarks the performance of RI-CC2 for ultrafast excited state dynamics in pyrazine by implementing analytical gradients and nonadiabatic couplings in Q-Chem to drive both vibronic coupling models and neural network-accelerated on-the-fly simulations, successfully reproducing experimental population decay times and identifying key vibrational modes and dark state participation in the internal conversion process.