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 proposes that integrating High-Performance Computing, Machine Learning, and High-Performance Quantum Computing into a unified framework will overcome the historical trade-off between chemical accuracy and computational scalability, thereby revolutionizing next-generation drug discovery and materials science.
This study demonstrates that Zn-Al alloy electrodes with 10–15 wt.% aluminum content significantly enhance alkaline oxygen evolution reaction performance by optimizing thermodynamic stability and electronic structure, achieving superior catalytic activity and lower overpotentials compared to pure zinc and other transition-metal-based catalysts.
This paper theoretically investigates the rotational excitation of asymmetric-top molecular ion isotopologues HO, HDO, and DO by electron impact using a combined framework of R-matrix scattering, multichannel quantum-defect theory, and adapted frame transformation and Coulomb-Born approximations to provide state-resolved cross sections and kinetic rate coefficients.
This paper presents a data-free reinforcement learning framework that integrates quantum mechanics calculations to generate novel molecules with desired properties, achieving significant speed-ups and success in both known and unexplored chemical spaces without relying on pre-trained datasets.
This paper presents a numerically exact, non-perturbative, and non-Markovian framework called AO-HEOM, derived from a three-dimensional rotationally invariant system-bath model, to investigate the quantum dynamics and linear absorption spectra of atomic orbitals in Coulomb potentials interacting with thermal baths.
This paper presents a machine learning framework that utilizes classical molecular dynamics trajectories to construct interpretable system-bath models with combined Brownian and Drude spectral distribution functions, enabling rigorous quantum simulations of ultrafast vibrational energy relaxation and dephasing in solution via the hierarchical equations of motion (HEOM).
This paper introduces complex-valued neural networks (CVNNs) as a physics-consistent framework that outperforms traditional real-valued models in predicting quantum dissipative dynamics by preserving essential amplitude-phase correlations, thereby achieving superior convergence, stability, and physical fidelity for open quantum systems.
This paper introduces HEOM-2DVS, a high-performance computational framework based on hierarchical equations of motion for simulating non-Markovian, anharmonic vibrational dynamics in molecular liquids, which is validated through the calculation of two-dimensional infrared spectra for coupled water modes.
This paper introduces V2Rho-FNO, a universal framework leveraging Fourier Neural Operators to directly map external potentials to electron densities, achieving accurate, zero-shot generalization across diverse and unseen molecular systems without relying on explicit atomic orbitals or handcrafted descriptors.
This study extends the evaluation of Quantum Theory Project (QTP) functionals to dynamic polarizabilities and long-range C dispersion coefficients, identifying TPSS0 and QTP01 as the top performers for dynamic polarizabilities and QTP01 and LC-QTP as the best within the QTP family for C coefficients among 25 tested exchange-correlation functionals.