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 demonstrates a technique to separate multiple nonlinear orders in two-dimensional electronic spectroscopy by varying pump pulse intensities, enabling the quantitative characterization of highly excited states, such as transition dipole moments and energy levels, in a squaraine dimer with excellent agreement between theory and experiment.
This study establishes an efficient, tuned DFT-TDDFT framework to screen heteroatom-doped organic dyes for dye-sensitized solar cells, revealing that electron-deficient boron doping effectively narrows the HOMO-LUMO gap and red-shifts charge-transfer excitations to enhance solar light harvesting.
This paper presents a Large Language Model agent integrated with AVEVA Process Simulation via the Model Context Protocol, which enables natural language interaction for automating complex chemical process tasks like analysis, optimization, and flowsheet synthesis, thereby enhancing both educational accessibility and professional efficiency while still requiring expert oversight.
This paper demonstrates that a recently proposed effective tuning protocol for range-separated hybrid functionals provides a computationally efficient and accurate alternative to conventional multi-step optimizations, yielding reliable starting points for one-shot and Bethe-Salpeter Equation calculations of ionization potentials and excitation properties across diverse molecular systems.
This study benchmarks five machine-learned interatomic potentials (SchNet, FieldSchNet, SO3Net, PaiNN, and MACE) for predicting molecular infrared spectra, finding that while all models achieve high accuracy on training data, the equivariant architectures (SO3Net, PaiNN, and MACE) demonstrate superior generalization to unseen systems, with PaiNN offering the best balance of efficiency and accuracy and MACE providing the highest spectral accuracy.
This perspective reviews and benchmarks methods that recover dynamic correlation for multireference wavefunctions using low-order reduced density matrices, finding that while MC-srPDFT is the most accurate DFT-based approach, linearized AC0 outperforms DFT methods and rivals expensive perturbation theory in predicting spin-state energetics for transition-metal complexes.
This paper theoretically establishes the real analyticity of single-reference coupled cluster amplitudes with respect to nuclear coordinates under non-degeneracy assumptions, identifies and mitigates regularity artifacts caused by canonical orbitals, and validates the feasibility of interpolating these amplitudes to reduce computational costs in molecular energy calculations.
This paper introduces mlip v2, a new generation of open-source software that enhances the efficiency, scalability, and flexibility of machine learning interatomic potentials through a redesigned modular API, a high-performance equivariant backend, and advanced capabilities like the eSEN architecture and improved electrostatics handling.
This paper introduces PASPT2, a novel partial-active-space multi-state multi-reference second-order perturbation theory that achieves strict size-extensivity and size-consistency for strongly correlated systems by employing a specialized reference-specific zeroth-order Hamiltonian to eliminate disconnected terms found in its parent coupled-cluster formulation.
DynaMate2 is an open-source, hierarchical agentic framework that democratizes AI-driven scientific workflows by allowing researchers to easily convert their existing expert-defined Python tools into AI-callable components without requiring the LLM to generate scientific code, thereby lowering the barrier to automation in domains like computational chemistry.