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 theoretical study reveals that electronic quantum geometry, specifically a crossing seam, can completely block an open reaction channel such as singlet fission in the H hydrogen chain, offering a new mechanism for controlling photochemical reactivity.
This paper introduces the "Gamma-Fit" analytical framework to accurately model the multiexponential photoluminescence decays of TADF emitters by accounting for conformational and kinetic heterogeneity in disordered solid-state films, while also validating these experimental findings through semiclassical Marcus-like computations that emphasize the critical role of conformational ensembles and multiple RISC-active triplet states in determining OLED efficiency.
This paper presents a consistently parameterized electrochemical-thermal-degradation model using PyBaMM to predict the capacity fade, state-of-health, and remaining useful life of NMC lithium-ion cells under 81 distinct calendar and cyclic ageing conditions, thereby providing mechanistic insights into the competing effects of solid-electrolyte interphase growth, lithium plating, and active material loss.
This paper introduces "Integral Decimation," a quantum-inspired algorithm that decomposes multidimensional integrals into a spectral tensor train representation to overcome the curse of dimensionality, enabling efficient and accurate calculations of free energy, entropy, and quantum dynamics in high-dimensional systems where conventional methods fail.
This study computationally demonstrates that the vibrational frequency shifts of probe molecules result from a competition where strong electrostatic interactions must overcome dominant Pauli repulsion to cause redshifting, while field inhomogeneity further modulates these shifts by either reinforcing redshifts or enhancing blueshifts depending on atomic mass and field sign.
This study demonstrates that incorporating three-body Axilrod–Teller–Muto interactions into the XDM dispersion model, particularly with the new Z-damping function, yields the most accurate exfoliation energies for layered materials using semi-local density-functional theory to date.
The paper introduces a spin-adapted neural network backflow (SA-NNBF) ansatz that enforces strict spin symmetry through a tensor-compressed sum-of-products spin eigenfunction and particle-hole duality, enabling highly accurate and efficient variational Monte Carlo simulations of strongly correlated systems like the FeMoco cofactor that outperform existing state-of-the-art methods.
This paper introduces a self-consistent, non-empirical Hessian-level meta-generalized gradient approximation functional (-PBE) that utilizes full density second derivatives to distinguish between atomic and bonding limits, demonstrating accurate chemisorption and molecular properties while highlighting remaining challenges in predicting bulk lattice constants.
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 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.