989 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 FRAMES, a novel training strategy that leverages minimal temporal information from just two consecutive molecular dynamics frames via an auxiliary loss function to significantly improve the energy and force prediction accuracy of molecular force fields, demonstrating that adding longer trajectory sequences can actually degrade performance.
This paper introduces a highly efficient hybrid ECA-NM optimization method that accurately determines reaction and diffusion parameters for chemical-diffusive models, enabling precise simulation of flame acceleration and deflagration-to-detonation transition with significantly reduced computational cost and error compared to traditional genetic algorithms.
This study demonstrates that treating Ti-6Al-4V alloy with 3M NaOH followed by hexadecyltrimethoxysilane (HDTMS) coating creates a highly hydrophobic surface (contact angle ~147°) that significantly enhances corrosion resistance in NaCl solutions, making it suitable for moisture-exposed applications.
By combining molecular dynamics simulations of Stockmayer particles with an effective thermodynamic potential, this study identifies a broad regime where dipolar chain sizes follow an exponential distribution, thereby providing a simplified thermodynamic description of self-assembly and delineating four distinct regions within the phase space.
This paper introduces the nudged elastic membrane method, a framework that efficiently constructs two-dimensional reduced potential energy surfaces using only energies and forces to capture complex multidimensional features, such as previously unreported saddle points, without requiring costly Hessian calculations.
This paper introduces efficient Fortran and Python software that utilizes machine-learned potentials to rapidly compute quartic force fields and perform quantum anharmonic VPT2 vibrational energy calculations for large molecules like aspirin, overcoming the prohibitive computational costs of traditional high-level electronic structure methods.
This paper presents a highly efficient time-domain analytic expression and its derivatives for chromatographic peak shapes derived from a stochastic-diffusive model incorporating multiple retention mechanisms, demonstrating significantly faster computation and superior fitting accuracy compared to existing models like the exponentially modified Gaussian.
This study presents the first comprehensive theoretical investigation into siloxane poisoning of catalytic gas sensors, utilizing an AI-guided framework to uncover decomposition mechanisms on noble metal surfaces and develop a descriptor-based model for predicting and designing anti-poisoning materials.
This paper introduces "surrogate functionals," a machine-learned approach for orbital-free density functional theory that achieves state-of-the-art ground-state density accuracy by optimizing solely on density data through a specialized loss function and adaptive sampling, thereby eliminating the computationally expensive orthonormalization steps required by prior methods.
This paper introduces Convex DFT (CVX-DFT), a novel framework that enforces convexity within the variational problem to guarantee unique, continuous electronic solutions across degenerate regions, thereby successfully restoring the correct topological structure of conical intersections and enabling robust, efficient non-adiabatic molecular dynamics simulations.