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 demonstrates that hybrid workflows combining machine-learned interatomic potentials (particularly MACE-OMol25) with transition-state search algorithms can achieve near-DFT accuracy for diverse chemical reactions while reducing computational costs by up to 96% compared to conventional methods.
This paper introduces the orthogonalized projector augmented-wave method combined with stochastic GW (OPAW-sGW), an implementation that preserves all-electron accuracy while enabling efficient quasiparticle band gap calculations on significantly coarser real-space grids compared to norm-conserving pseudopotential approaches.
This study utilizes machine-learning-enabled simulations to demonstrate that while anisotropic electronic friction significantly influences the rate of energy transfer and reaction probabilities during laser-driven hydrogen recombination on copper, the final molecular energy distributions are primarily governed by the potential energy landscape rather than the friction anisotropy.
This paper introduces AcepKa, a thermodynamically consistent application within the PlayMolecule AI platform that leverages the Uni-pKa framework and a retrained Uni-Mol backbone to accurately predict pKa values and generate dominant protonation states, while also featuring engineering advancements like a high-speed GPU-accelerated conformer generator and 3D-aware protonation tools for drug discovery.
The paper introduces Hyperion, a high-performance, GPU-accelerated quantum emulator that combines exact sparse matrix-vector kernels with a novel partitioned SV-MPS strategy to enable accurate simulations of strongly correlated chemical systems ranging from 32 to 40 qubits, thereby bridging the gap between current quantum hardware limitations and the need for rigorous algorithm validation.
This paper introduces two molecular reference frames to define oriented external electric fields and derives analytic nuclear gradients within these frames, enabling accurate geometry optimizations and systematic studies of field-controlled molecular structure and reactivity.
This paper employs the matrix-tree theorem to establish kinetic-independent thermodynamic bounds on symmetry breaking in linear and catalytic biochemical systems, demonstrating how non-equilibrium driving forces constrain emergent selection phenomena and reaction-diffusion patterns in both closed and open networks.
This paper employs Markovian Lindblad quantum dynamics to model electron transport in incoherently-driven molecular nanojunctions, demonstrating that the theory successfully reproduces key experimental features like negative differential conductance and Coulomb blockade while predicting light-induced currents in specific two-site configurations.
This paper employs a time-dependent Newns-Anderson-Schmickler model with Keldysh Green's functions and semiclassical trajectories to demonstrate how adsorbate motion and solvent coupling non-adiabatically suppress electron transfer while facilitating energy dissipation into electron-hole pairs, ultimately deriving an analytical expression for the average energy transfer rate in the slow-motion limit.
This paper presents a hybrid atomistic-parametric decoherence model that combines molecular dynamics-derived -tensor fluctuations with a magnetic field noise term to accurately predict the relaxation and dephasing times of copper porphyrin molecular spin qubits across various magnetic fields, resolving discrepancies between purely atomistic simulations and experimental data.