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 proposes a hierarchical decontraction strategy using distinct exponents in Gaussian expanded free complement functions to mitigate the exponential scaling of variational parameters with electron count at low expansion orders, thereby delaying the computational complexity until higher orders.
This paper demonstrates that time gating and kinetic-energy filtering in photoelectron-detected two-dimensional electronic spectroscopy can effectively disentangle single- and biexciton dynamics, recovering information obscured by exciton-exciton annihilation and enabling the direct inference of annihilation processes.
Aitomia is an AI-powered intelligent assistant platform that integrates large language model agents with the MLatom software to democratize and accelerate atomistic and quantum chemical simulations by enabling both experts and non-experts to autonomously set up, run, and analyze complex computational workflows through a user-friendly chat interface.
This paper introduces MDtrajNet, a novel neural network architecture and pre-trained foundational model that directly generates molecular dynamics trajectories across chemical space without sequential force calculations, achieving simulation speeds up to two orders of magnitude faster than conventional methods while maintaining accuracy comparable to ab initio simulations.
This paper introduces a novel machine learning framework featuring NAC-specific descriptors and a phase-correction procedure that achieves unprecedented accuracy () in predicting nonadiabatic coupling vectors, enabling robust and efficient fully ML-driven fewest-switches surface hopping simulations for photochemical processes.
This paper resolves the Blanc-Cances instability of the Wang-Teter nonlocal kinetic energy density functional in isolated systems by introducing a density-functional-dependent kernel, thereby achieving an order-of-magnitude accuracy improvement for single atoms while maintaining superior performance in bulk metals.
This study demonstrates that dielectric relaxation in solid and liquid water is governed by classic proton transfer over an energy barrier rather than molecular reorientation, as evidenced by cross-validated measurements showing a constant H₂O/D₂O relaxation rate ratio of 2.0 across a wide frequency and temperature range.
This paper generalizes Förster resonance energy transfer theory to ultrafast nonlinear regimes by deriving a formally exact, time-non-local master equation that captures transient coherent effects and initial condition slippage, thereby improving upon traditional formulations and extending validity to limits of vanishing system-bath coupling.
This paper introduces TT-Metadynamics, a scalable method that compresses the bias potential in metadynamics into a low-rank tensor train representation using a sketching algorithm, thereby enabling efficient free energy exploration in high-dimensional systems with up to 14 collective variables without the exponential computational cost of standard approaches.
This paper proposes a unified geometric framework for molecular reaction dynamics that integrates the variational principle, curved spacetime physics, and AI techniques to construct a nuclear Hamiltonian in nonzero curvature, thereby enabling the natural introduction of geometric phases and gauge fields while offering new optimization-based insights for solving the Schrödinger equation.