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 introduces Coarse-Grained Boltzmann Generators (CG-BGs), a framework that combines flow-based generative models with learned potentials of mean force to enable efficient, asymptotically correct equilibrium sampling of large molecular systems in reduced coarse-grained coordinate spaces.
The paper introduces CSP-MACE-Å, a machine learning interatomic potential that decomposes total energy into intra- and intermolecular components to achieve DFT-level accuracy in crystal structure prediction while running orders of magnitude faster, thereby enabling more robust derisking of solid forms through extensive candidate evaluation and free energy calculations.
By combining quantum field theory and machine learning force fields, this study reveals that interatomic forces in large molecules exhibit robust scatter and substantial anisotropy that grow with system size, challenging traditional empirical models and suggesting a shift toward identifying interaction "hotspots" to better understand molecular folding and dynamics.
The paper introduces M\=oLe-, an equivariant machine learning model that jointly predicts both right- and left-hand coupled-cluster amplitudes from localized Hartree-Fock orbitals to efficiently generate accurate energies, forces, and a wide range of response properties while preserving the size-extensivity and locality of traditional CCSD theory.
This paper proposes a novel evaluation framework that decomposes mixture-property prediction errors into pure-component and non-ideal interaction components to reveal that strong absolute accuracy often masks poor generalization to unseen molecules and non-ideal mixture behaviors.
This paper demonstrates that dephasing noise in a multimode cavity induces a hierarchical sequence of dynamical regimes and unexpectedly enhances the ballistic expansion of polariton wave-packets, sustaining this spreading far beyond the microscopic dephasing time.
This paper introduces "Cage Water," an analytical statistical mechanical model that explains water's thermophysical anomalies, including its controversial liquid-liquid supercooling transition, as simple transitions among three distinct molecular bonding states: van der Waals, pairwise hydrogen bonding, and multi-body cooperative caging.
This paper presents a resource-efficient Szegedy quantum walk construction for the Metropolis-Hastings algorithm that avoids the high qubit overhead of reversible computing by directly following classical proposal-acceptance logic, thereby enabling a practical end-to-end quadratic speedup for Markov Chain Monte Carlo simulations.
This numerical study utilizes full-scale FDTD simulations to demonstrate that proximity to metal interfaces and cavity environments significantly alters Raman scattering signals through mechanisms beyond standard SERS enhancement, including modified local fields, cavity-induced excited state population trapping, lineshape broadening via relaxation channels, and interference effects like Rabi contraction.
This paper benchmarks perturbative quantum master equation and mixed quantum-classical methods against fully quantum dynamics for hot exciton cooling in CdSe nanocrystals, revealing that while the former captures ultrafast diabatic mixing, the mapping approach to surface hopping (MASH) provides the most consistent agreement across all relaxation regimes.