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 presents a deep learning framework based on transformer architecture that successfully achieves automated de novo structure elucidation for organic molecules with up to 40 non-hydrogen atoms using only one-dimensional H and C NMR spectra, correctly identifying the target molecule within the top 15 predictions in 60.4% of cases.
This study utilizes Electron Paramagnetic Resonance (EPR) spectroscopy to directly quantify radical species and demonstrate that a boron-doped diamond anode paired with a NaNbO3@CeO2-modified gas diffusion electrode achieves significantly faster and more complete paracetamol degradation via electro-Fenton processes compared to platinum anodes, thereby establishing a validated mechanistic framework for optimizing electrochemical water treatment.
This paper demonstrates that stochastic classical trajectories, evolved in the complex plane via a Matsubara generalized Langevin equation, can successfully equilibrate to the exact thermal state of continuous-variable open quantum systems, even beyond the weak-coupling limit.
This paper proposes an efficient analytic continuation method that constructs Bethe-Salpeter absorption spectra within specific energy windows by iteratively calculating polarizability tensors at a coarse set of complex frequencies to form a matrix-valued continued-fraction representation, which is then validated across diverse molecular and nanoscale systems.
This study demonstrates that a gas diffusion electrode modified with 3% nano-octahedral Fe3O4 supported on Vulcan XC72 significantly enhances hydrogen peroxide generation and effectively degrades over 70% of endocrine-disrupting progestins (levonorgestrel and gestodene) in water through optimized solar and anodic-assisted electro-Fenton processes.
This study demonstrates that low-loading CeO and CeOMnO nanoparticles supported on Vulcan XC-72 carbon significantly enhance the selectivity and activity for sustainable hydrogen peroxide electrogeneration via the two-electron oxygen reduction reaction, with the 1% CeOMnO/C catalyst achieving up to 90% selectivity.
This paper proposes a unified representation-level framework for equilibrium quantum many-body theory that characterizes different methods as encoders mapping admissible states to specific variables, thereby clarifying the conditions for exact reconstruction and unifying concepts like functionals, kernels, and quantum embedding through the analysis of state fibers and task-relevant information.
This paper introduces PoseBusters, a validation tool demonstrating that current deep learning-based protein-ligand docking methods often fail to generate physically plausible structures or generalize to novel sequences, thereby underperforming classical docking tools that better incorporate essential physical principles.
This paper introduces BayesMBAR, a Bayesian generalization of the multistate Bennett acceptance ratio (MBAR) method that computes free energy posterior distributions to provide more accurate uncertainty estimates and allow for the incorporation of prior knowledge, such as surface smoothness, into free energy calculations.
This study benchmarks machine learning foundation potentials against quantum chemistry methods for predicting molecular redox potentials, revealing their high accuracy for proton-coupled electron transfer but limitations for multi-electron transfers, and proposes a hybrid workflow combining efficient potential-based geometry optimization with single-point DFT energy refinement to enable scalable high-throughput screening.