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 LOREM, a graph neural network architecture that employs equivariant messages for long-range interactions to overcome the limitations of cutoff-based models in capturing non-local physical effects like electrostatics and electron delocalization, achieving consistent and superior performance across diverse datasets without requiring dataset-specific hyperparameter tuning.
This computational study utilizes pair Coupled Cluster Doubles (pCCD) to demonstrate that tri-nitrogen doping at the bridge of carbazole-based organic dyes maximizes directional donor-to-acceptor charge transfer (42.6%), identifying this specific variant as the most promising candidate for dye-sensitized solar cells.
This study demonstrates that the iterative qubit coupled-cluster (iQCC) algorithm, simulated at unprecedented scale on classical hardware, achieves superior accuracy over leading classical methods for predicting the excited states of complex organometallic compounds, thereby establishing a threshold of approximately 200 logical qubits where quantum advantage in computational chemistry may emerge.
This paper provides a guide for NLP researchers and interdisciplinary scientists on popular digital molecular representations inspired by natural language processing and their applications in AI-driven chemistry and materials science.
This paper advocates for the integration of agentic AI, machine learning workflows, and self-driving laboratories with high-throughput experimentation to create a virtuous, data-driven cycle for accelerating catalyst design, mechanistic understanding, and scale-up across diverse catalytic systems in chemical reaction engineering.
This paper introduces Drifting Models for molecular conformation generation, leveraging a novel Drifting Force Identity to incorporate physical force labels into one-step sampling, thereby achieving million-fold acceleration over traditional molecular dynamics while maintaining perfect structural validity and Boltzmann distribution accuracy.
This paper develops a reaction-coordinate-resolved information-theoretic framework using Partial Information Decomposition to analyze chemical reactivity, demonstrating how mutual information between electronic readouts and geometric progress variables reveals distinct redundant, unique, and synergistic signatures of bonding evolution in prototypical S2 reactions.
This paper presents a novel Bayesian inference framework that integrates molecular dynamics simulations and polarisation analysis to overcome the limitations of conventional fitting methods, successfully resolving the previously ambiguous anisotropic rotational motion of liquid benzene and establishing a new paradigm for understanding molecular dynamics in catalysis and energy materials.
This paper investigates how the characteristics of laser pulses (long versus ultrashort) shape the initial excited molecular state, demonstrating that the exact factorization framework challenges standard Born-Huang concepts like population transfer and vertical excitation by revealing a more complex dependence on the light source.
This paper introduces a scalable quantum-nuclei-corrected NMR crystallography approach (QNC-NMR) that leverages the machine-learning potential PET-MOLS to generate quantum ensembles, thereby significantly improving the accuracy of chemical shielding predictions for hydrogen-bonded protons and enabling applications to amorphous materials without empirical corrections.