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.
Using VUV synchrotron radiation and ion-electron coincidence spectroscopy, this study accurately determines the adiabatic ionization energy of DABCO, resolves two vibrational progressions in its ground state cation, and attributes the vibrational dependence of the photoelectron angular distribution anisotropy to scattering mediated by high-lying Rydberg states.
This paper introduces a new open-source implementation of the anti-Hermitian contracted Schrödinger equation (ACSE) that offers a scalable, accurate, and robust method for simulating all-electron correlation in both ground and excited states of molecular systems, overcoming the limitations of traditional perturbative approaches by utilizing the exact electronic Hamiltonian.
This study employs multireference quantum-chemical calculations to demonstrate that the torsional angle between monomer units, rather than regio-connectivity, is the primary factor governing the efficiency of singlet fission versus spin-orbit charge transfer intersystem crossing in aza-BODIPY dimers.
This paper proposes a Central-Peripheral Distillation (CPD) algorithm that significantly improves the training efficiency of machine learning force fields in phase transition regimes by distilling a highly diverse dataset of just 200 representative and critical configurations, enabling accurate simulation of liquid hydrogen's structural and dynamical properties.
This paper introduces interacting-bath dynamical embedding theory (ibDET), a scalable molecular extension that accurately computes charged excitation energies at the GW and EOM-CCSD levels with significantly reduced computational cost by constructing frequency-dependent bath representations from atom-centered impurities.
This study demonstrates that while photodissociation of ammonia in cometary ice analogs can account for most observed molecular nitrogen, the substantial carbon monoxide abundances in many comets likely require low-temperature entrapment rather than in-situ chemical processing.
This paper presents a thermodynamically consistent entropy scaling framework that successfully predicts self- and mutual diffusion coefficients in fluid mixtures across a wide range of states, including non-ideal systems, by leveraging pure component self-diffusion data, infinite-dilution coefficients, and molecular-based equations of state.
This paper presents the development and application of a machine-learned interatomic potential to accurately model the structure, transport, and reactivity of hydrated Nafion and platinum catalysts in fuel cells, while highlighting the current limitations of active learning and long-timescale transport simulations in complex multicomponent systems.
This study systematically compares pre-training/fine-tuning and multi-headed training strategies for machine-learned force fields, revealing that while pre-training offers superior accuracy through method-specific representations, multi-headed training provides a practical trade-off by learning method-independent representations that enable cost-efficient universal models.
The paper introduces SMILE-CP, a computationally efficient, macro-dipole-constrained learning scheme that accurately infers atomic charges from standard DFT data to overcome thermal fluctuations and enable reliable, bias-controlled simulations of electrochemical interfaces.