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 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 introduces a semidefinite machine learning framework that combines input convex neural networks with semidefinite programming to learn a data-driven, vertex-based approximation of the -representable two-electron reduced density matrix (2-RDM) boundary, enabling direct variational calculations with accuracy comparable to higher-order positivity constraints but at the computational cost of two-positivity methods.
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 study evaluates the Boltz-2 AI model for drug discovery and finds that while it offers rapid structural predictions, it lacks the energetic accuracy and resolution required for reliable lead identification, necessitating the continued use of physics-based methods for refinement.
This paper introduces RESOLUTE, a novel Ramsey correlation spectroscopy protocol utilizing phase cycling and population imbalance storage that extends effective coherence time beyond the standard limit, enabling the detection of low-frequency signals such as C nuclear spin precession in regimes previously inaccessible to single quantum sensors.
This paper proposes a constructive, physics-based approach to designing interatomic potentials by leveraging density functional theory theorems and analytic constraints to build interpretable latent space embeddings that formally couple electronic and atomic scales, thereby addressing the limitations of current machine learning models regarding combinatorial complexity and unseen bonding motifs.
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.
The paper introduces SAFT-P, a plaquette-level extension of Statistical Associating Fluid Theory that improves the modeling of self-assembly in patchy colloids by retaining topology-dependent information and accurately predicting critical points and coexistence curves for particles with identical valence but different patch layouts.