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.
This paper argues that the recent emergence of foundation machine learning interatomic potentials, which combine quantum accuracy with force-field speed without requiring system-specific training, will likely replace density functional theory (DFT) as the primary method in computational chemistry within the next decade.
The paper introduces VIANA, a novel tri-pillar framework that integrates molecular graph structures, PCA-distilled semantic odor character embeddings, and biological dose-response logic to significantly outperform traditional models in predicting odorant intensity by effectively bridging molecular informatics with human sensory perception.
TUNA is an open-source, streamlined quantum chemistry program designed specifically for atoms and diatomic molecules that unifies a broad range of electronic structure methods under a single principle of numerical differentiation to provide a transparent platform for teaching, benchmarking, and method development.
This paper introduces a rigorous, atom-resolved framework for decomposing second-harmonic generation contributions in nonlinear-optical crystals, revealing that two-center terms dominate the response while specific cooperative interactions between anionic frameworks and cation sublattices drive the optical properties of materials like BBO, LBO, and KBBF.
This paper introduces a residence-time approach (RTA) that efficiently determines position-dependent diffusivities from biased molecular dynamics simulations by calculating mean first-exit times, offering a practical alternative to conventional fluctuation-based methods without requiring additional restrained simulations or noisy time-correlation integrations.
This paper presents a large-scale experimental study on IQM's 24-qubit superconducting processor demonstrating that hybrid quantum-classical approaches, specifically combining Sample-based Quantum Diagonalization with various ansätze and Density Matrix Embedding Theory, can achieve chemically accurate ground-state energies and full potential energy surfaces for molecules ranging from simple benchmarks to pharmacologically relevant systems like amantadine.
This paper investigates how stochastic resetting influences competitive first-passage outcomes in non-Markovian systems with memory effects, demonstrating through a continuous-time random walk framework that resetting can selectively enhance desired events and suppress fluctuations in conditional first-passage times depending on the underlying waiting-time statistics.
This paper introduces an efficient Auxiliary-Field Quantum Monte Carlo method utilizing isometric tensor hypercontraction to diagonalize Coulomb interactions, which achieves reduced computational complexity and high-accuracy ground-state energies for strongly correlated systems like the H10 chain and benzene, outperforming standard AFQMC while matching the precision of high-level wavefunction methods.
This paper evaluates three machine learning-based gradient estimators (Gumbel-Softmax Straight-Through, Score Function, and Alternative Path) for enabling efficient parameter inference in discrete stochastic kinetic models simulated via the Gillespie algorithm, demonstrating that while the Gumbel-Softmax estimator generally performs well, the other methods offer superior robustness in challenging regimes where variance diverges.
This paper utilizes the recently developed small-tensor-product (STP) decomposition framework to perform numerically exact relativistic full configuration interaction calculations in a 100-orbital space, thereby establishing a definitive benchmark with rigorous error bounds to assess the accuracy, variationality, and convergence of relativistic coupled cluster and density matrix renormalization group methods.