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 extends the Kähler manifold formalism to CASSCF theory to derive linear response equations and develop a robust state-specific method for computing electronic excitation energies, demonstrating its effectiveness on molecular systems while highlighting challenges arising from the theory's inherent nonlinearity.
This paper introduces the antisymmetrized geminal power configuration interaction (AGP-CI) method, which extends the AGP framework to capture inter-geminal correlations via a computationally efficient linear combination of AGPs, demonstrating superior accuracy over standard LC-AGP in strongly correlated systems like the Hubbard model and small molecules.
This paper employs stochastic density functional theory to extend the classical Debye-Falkenhagen prediction of frequency-dependent conductivity from dilute to concentrated electrolytes by incorporating hard-core repulsion effects, while also addressing the challenges in experimentally observing this phenomenon.
Using a novel combination of electron-microscopy-based vibrational and core-level spectroscopy, researchers mapped the nanoscale distribution and chemical composition of uncommon, nitrogen-containing organic matter in asteroid Ryugu, revealing soluble, aliphatic components and NHx functional groups that likely formed in the outer solar nebula or via fluid reactions on the parent body.
By combining machine-learning molecular dynamics with vibrational sum-frequency generation simulations, this study reveals that the apparent hydrophilicity of monolayer graphene on hydrophilic substrates stems from thermodynamically favorable intercalated water molecules causing signal cancellation, rather than wetting transparency, while multilayer graphene lacks this intercalation.
This paper introduces a variational nodal partition of correlation energy that defines static correlation as the energy penalty of constraining the wavefunction to mean-field nodes, thereby providing a rigorous, method-independent framework to distinguish static, dynamic, and strong correlation and explain the variable accuracy of fixed-node diffusion Monte Carlo.
This review examines the application of generative AI to the inverse design of inorganic compounds, analyzing how data-representation-model pipelines address unique challenges like symmetry and electronic structure across diverse systems while outlining future directions for benchmarking and synthesizability.
This paper derives a leading-order semiclassical periodic orbit trace formula for local microcanonical out-of-time-order correlators (OTOCs) in systems with index-1 saddles, expressing the scrambling rate as a coherent sum over unstable periodic orbits on the Normally Hyperbolic Invariant Manifold (NHIM) to establish a theoretical mechanism for mode-selective control of quantum information scrambling.
This paper introduces a hierarchical generative optimization framework that couples genetic algorithms with generative models to enable the automated, data-driven design of complex multi-component systems, successfully demonstrating a 30% reduction in activation barriers for catalytic environments through joint optimization of molecular composition and spatial arrangement.
This paper introduces X-MACE, a transferable machine-learning potential combined with curvature-driven surface hopping, to efficiently screen fluorescent protein chromophores and reveal how steric crowding and conjugation extension govern their excited-state dynamics and fluorescence properties.