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.
The paper introduces HEroBM, a deep equivariant graph neural network that utilizes a hierarchical, local-principle-based approach to achieve accurate, transferable, and universal backmapping from coarse-grained to all-atom molecular representations across diverse chemical systems.
This paper introduces a novel, scalable, and size-intensive "Double Configuration Interaction Singles" method that utilizes a perturbative treatment of the electronic Hessian to variationaly account for orbital relaxation, thereby significantly improving the accuracy of charge-transfer excitation energies and single bond dissociation descriptions while maintaining a mean-field computational cost.
This paper introduces TBSCI, a fully distributed diagonalization framework utilizing a tensor-product bitstring representation and novel communication strategies that overcomes memory bottlenecks to enable scalable selected configuration interaction calculations on over 2.5 million cores while demonstrating the method's ability to achieve near-full configuration interaction accuracy with a compact wavefunction.
This review surveys recent advances in using atomistic molecular dynamics simulations to characterize RNA conformational dynamics across various contexts, emphasizing the role of enhanced sampling, integrative approaches, and artificial intelligence in improving the precision and accuracy of structural ensembles.
This paper demonstrates that a classical solver utilizing a clustering approximation can efficiently simulate NMR spectra with linear resource scaling, thereby challenging the assumption that such problems inherently require exponential resources and necessitating a re-evaluation of the potential for quantum advantage in this domain.
This study employs rigidity-based spring network analysis on thousands of metal-organic frameworks to reveal that while most are formally over-constrained, they cluster near mechanical instability due to accidental geometric modes, suggesting a design principle rooted in near-criticality that can be stabilized by tuning long-range constraints.
This paper investigates the significant coupling between subvalence correlation and higher-order post-CCSD(T) effects on the total atomization energies of first- and second-row molecules, leading to a proposed "W5 theory" protocol that yields revised, highly accurate thermochemical values consistent with Active Thermochemical Tables (ATcT).
This paper introduces a unified variational framework that extends Configuration Interaction Singles (CIS) through orbital optimization, linear-response corrections, and spin-projection techniques to systematically improve the description of both ground and excited states, particularly in strongly correlated systems and bond dissociation scenarios.
LatentChem introduces a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning in continuous latent space which spontaneously emerges as a more efficient and accurate alternative to explicit Chain-of-Thought, achieving a 59.88% win rate and 10.84 speedup over baselines.
This study demonstrates that aqueous chlorine dioxide radicals can serve as transient spin labels for poly(ethylene terephthalate), enabling the identification of plastic types and the measurement of molecular diffusion within the polymer matrix through electron spin resonance spectroscopy.