794 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 a stochastic cluster expansion framework that enables near-DMRG accuracy for total correlation energies in large condensed-phase systems by combining exactly treated subspaces with randomly sampled environment orbitals, thereby eliminating the need for prior active space selection and facilitating high-accuracy studies of chemical processes in complex environments.
This paper introduces the effective Hamiltonian-based TEMPO (EH-TEMPO) algorithm, which reformulates non-Markovian path integral simulations as an imaginary time evolution to achieve significant computational speedups and GPU acceleration while maintaining numerically exact accuracy.
This study establishes a quantitatively validated theoretical framework combining TD-DFT, CC2, and Frenkel exciton models to accurately link the structural features of paracyclophanes with their optical and electronic properties, offering design principles for next-generation optoelectronic materials.
This paper demonstrates that the Ishizaki–Tanimura correction in Hierarchical Equations of Motion (HEOM) corresponds to separating smooth and Brownian contributions to the bath's path-integral radius of gyration, and proposes a modified correction alongside an "A4" pole-fitting algorithm to significantly improve the efficiency of HEOM simulations for fast baths and low temperatures.
This paper presents a plane-wave implementation of auxiliary-field quantum Monte Carlo within the PAW formalism in VASP that operates at the complete basis set limit with cubic scaling, achieving high-accuracy predictions of lattice constants and bulk moduli for C, BN, BP, and Si by correcting deficiencies in MP2 and RPA methods.
This paper introduces a numerically robust, closed-form effective classical potential based on a mean-field treatment of quantum fluctuations about the path starting point, which accurately estimates quantum thermal expectation values for position-dependent observables in systems with harmonic support.
This paper presents an efficient method for computing accurate nuclear forces within the phaseless auxiliary-field quantum Monte Carlo framework using automatic differentiation, which is then combined with machine learning potentials to successfully perform geometry optimizations and transition state searches that agree closely with coupled-cluster reference values.
This paper introduces Stoichiometrically-Informed Symbolic Regression (SISR), a novel data-driven method that combines differential and genetic optimization to accurately extract chemical reaction mechanisms, kinetic equations, and rate constants from time-series concentration data, even in the presence of noise.
This study reports the on-surface synthesis and atomic-scale characterization of antiferromagnetic S=1/2 quantum spin rings composed of [2]triangulene units on Au(111), revealing that while planar six-membered rings exhibit uniform excitation gaps describable by a Heisenberg model, distorted five-membered rings display asymmetric spin ground states due to structural distortion-induced degeneracy lifting.
This paper develops an analytical framework to evaluate stochastic Verlet-type integrators by deriving closed-form expressions for their accuracy in simulating diffusion, drift, and harmonic potential sampling, ultimately identifying the GJ set of integrators as the most robust option for high-quality thermodynamic simulations.