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 introduces a symmetry-based multi-reference perturbation theory (SBPT) that leverages enhanced symmetries in a reference Hamiltonian to significantly reduce computational costs in both classical configuration interaction and quantum computing applications, while offering scalable solutions and improved robustness for various molecular systems.
This paper introduces NATPS, a novel method that combines the time-reversible Mapping Approach to Surface Hopping (MASH) dynamics with transition path sampling to efficiently simulate rare nonadiabatic events and provide mechanistic insights into photochemical processes while significantly reducing computational costs compared to brute-force approaches.
The paper introduces FragFM, a hierarchical framework utilizing fragment-level discrete flow matching and a stochastic fragment bag strategy to achieve efficient, scalable, and property-controllable molecular generation, validated through a new Natural Product Generation (NPGen) benchmark where it outperforms existing atom-based methods.
This paper introduces a Bayesian hierarchical modeling framework to rigorously evaluate the performance of the Dimer method across 500 molecular systems, revealing that while Conjugate Gradient offers superior robustness over L-BFGS, the nuanced interplay of optimization strategies supports the development of adaptive, context-dependent workflows rather than a single universal solution.
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 paper proposes a machine learning approach that learns data-driven, structure-preserving (symplectic and time-reversible) maps equivalent to the mechanical action of a system, enabling accurate long-time-step molecular dynamics simulations that eliminate the energy conservation and equipartition artifacts typical of non-structure-preserving ML predictors.
This paper introduces metatensor and metatomic, two foundational software libraries designed to bridge the gap between traditional atomistic modeling tools and modern machine learning frameworks by providing interoperable, portable data structures and model interfaces that facilitate the adoption of ML in atomic-scale simulations.
This paper introduces the Tensor Atomic Cluster Expansion (TACE), a unified atomistic machine learning framework that employs irreducible Cartesian tensors to efficiently model both scalar and tensorial observables without complex angular-momentum coupling, demonstrating robust accuracy and scalability across diverse chemical systems and tasks.
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 paper introduces a novel two-dimensional visualization framework that maps reaction trajectories onto a permutation-corrected RMSD plane with gradient-enhanced Gaussian Process energy interpolation, enabling more effective comparison and validation of optimization histories across different computational methods for complex chemical reactions.