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We present a weighted-graph-theoretic approach to adaptively compute contributions from many-body approximations for smooth and accurate post-Hartree-Fock (pHF) molecular dynamics (AIMD) of highly fluxional chemical systems. This approach is ONIOM-like, where the full system is treated at a computationally feasible quality of treatment (density functional theory (DFT) for the size of systems considered in this publication), which is then improved through a perturbative correction that captures local many-body interactions up to a certain order within a higher level of theory (post-Hartree-Fock in this publication) described through graph-theoretic techniques. Due to the fluxional and dynamical nature of the systems studied here, these graphical representations evolve during dynamics. As a result, energetic "hops" appear as the graphical representation deforms with the evolution of the chemical and physical properties of the system. In this paper, we introduce dynamically weighted, linear combinations of graphs, where the transition between graphical representations is smoothly achieved by considering a range of neighboring graphical representations at a given instant during dynamics. We compare these trajectories with those obtained from a set of trajectories where the range of local many-body interactions considered is increased, sometimes to the maximum available limit, which yields conservative trajectories as the order of interactions is increased. The weighted-graph approach presents improved dynamics trajectories while only using lower-order many-body interaction terms. The methods are compared by computing dynamical properties through time-correlation functions and structural distribution functions. In all cases, the weighted-graph approach provides accurate results at a lower cost.
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http://dx.doi.org/10.1021/acs.jctc.0c01287 | DOI Listing |
Comput Biol Chem
September 2025
Department of Mathematics, Gour Mahavidyalaya, Malda 732142, India. Electronic address:
This research proposes an advanced technique to manipulating milk flow and its thermal characteristics through a dynamic electromagnetic pathway, effectively managing the non-linear thermal behavior of milk. This study employs advanced artificial intelligence (AI) to create a sophisticated analytical framework for modeling the complex interactions between milk flow, hybrid nanoparticles (Ag-ZnO), and dynamic thermal conditions in a squarely activated electromagnetic tunnel. The research focuses on optimizing key steps in dairy manufacturing-microbial reduction and texture stabilization by analyzing the behavior of Ag-ZnO/milk under oscillating thermal amplification, incorporating radiant heat and Darcy drag effects.
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View Article and Find Full Text PDFR Soc Open Sci
July 2025
Big Data Institute, Nuffield Department of Population Health, Oxford, UK.
The global burden of multimorbidity is increasing yet poorly understood, owing to insufficient methods for modelling complex systems of conditions. In particular, hepatosplenic multimorbidity has been inadequately investigated. From 17 January to 16 February 2023, we examined 3186 individuals aged 5-92 years from 52 villages across Uganda within the SchistoTrack Cohort.
View Article and Find Full Text PDFR Soc Open Sci
July 2025
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Uusimaa, Finland.
Reproducibility is recognized as essential to scientific progress and integrity. Replication studies and large-scale replication projects, aiming to quantify different aspects of reproducibility, have become more common. Since no standardized approach to measuring reproducibility exists, a diverse set of metrics has emerged and a comprehensive overview is needed.
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