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.
The paper introduces AceFF, a state-of-the-art, pre-trained machine learning interatomic potential based on the TensorNet2 architecture that achieves DFT-level accuracy and high-throughput speed for small molecule drug discovery while explicitly supporting essential medicinal chemistry elements and charged states.
This paper generalizes Förster resonance energy transfer theory to ultrafast nonlinear regimes by deriving a formally exact, time-non-local master equation that captures transient coherent effects and initial condition slippage, thereby improving upon traditional formulations and extending validity to limits of vanishing system-bath coupling.
This paper introduces TT-Metadynamics, a scalable method that compresses the bias potential in metadynamics into a low-rank tensor train representation using a sketching algorithm, thereby enabling efficient free energy exploration in high-dimensional systems with up to 14 collective variables without the exponential computational cost of standard approaches.
This study employs large-scale molecular dynamics simulations with machine-learned interatomic potentials to reveal that hydroxide ion transport in commercial anion-exchange membranes is critically dependent on hydration levels, where sufficient water content forms connected hydrogen-bond networks that enable efficient long-range diffusion, thereby linking nano-scale structural dynamics to macroscopic performance for green hydrogen production.
This paper proposes a unified geometric framework for molecular reaction dynamics that integrates the variational principle, curved spacetime physics, and AI techniques to construct a nuclear Hamiltonian in nonzero curvature, thereby enabling the natural introduction of geometric phases and gauge fields while offering new optimization-based insights for solving the Schrödinger equation.
This paper presents a systematically improvable numerical atomic orbital basis set constructed by contracting truncated spherical waves to minimize kinetic operator spillage, which enhances transferability and eliminates spurious periodic interactions while achieving high precision across diverse molecular and bulk properties.
This paper introduces a framework combining the mapping approach to surface hopping (MASH) with transition-path sampling to efficiently simulate rare nonadiabatic reactions by generating unbiased reactive pathways that enable the detailed analysis of reaction mechanisms and rate constants.
This paper presents a universal detection method utilizing quantum point-contact sensors based on conductance quantization to rapidly and selectively identify a wide spectrum of pollutants, including heavy metal ions and organic solvents, in liquid media at trace concentrations.
This paper reviews the major advancements of the open-source PySCF framework over the past decade, highlighting new modules, methodologies, infrastructure changes, and performance benchmarks since its 2020 overview.
This study integrates shock tube experiments and modeling to characterize methane pyrolysis for co-producing hydrogen and carbon black, providing critical benchmarks on gas-phase kinetics, particle formation dynamics, and nanostructure evolution to improve predictive models.