Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification.
Physical Chemistry, Chemical Physics - PCCP, 2023 Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems.
Qiaofeng Li +3 more
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Improving Chemical Reaction Prediction with Unlabeled Data
Molecules, 2022 Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data.
Yu Xie +4 more
doaj +1 more source
Large deviations theory for Markov jump models of chemical reaction networks [PDF]
The Annals of Applied Probability, 2017 We prove a sample path Large Deviation Principle (LDP) for a class of jump processes whose rates are not uniformly Lipschitz continuous in phase space. Building on it we further establish the corresponding Wentzell-Freidlin (W-F) (infinite time horizon ...
A. Agazzi, A. Dembo, J. Eckmann
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Identification of functional differences in metabolic networks using comparative genomics and constraint-based models. [PDF]
PLoS ONE, 2012 Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms.
Joshua J Hamilton, Jennifer L Reed
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The Gompertz model revisited and modified using reaction networks: Mathematical analysis
Biomath, 2021 In the present work we discuss the?usage of the framework of chemical reaction networks for the construction of dynamical models and their mathematical analysis.
Svetoslav Marinov Markov
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Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case
Molecules, 2023 Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction path
Ruben Staub +4 more
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The Smallest Multistationary Mass-Preserving Chemical Reaction Network [PDF]
Algebraic Biology, 2008 Biochemical models that exhibit bistability are of interest to biologists and mathematicians alike. Chemical reaction network theory can provide sufficient conditions for the existence of bistability, and on the other hand can rule out the possibility of
Anne Shiu
semanticscholar +1 more source
Persistence and stability of generalized ribosome flow models with time-varying transition rates.
PLoS ONE, 2023 In this paper some important qualitative dynamical properties of generalized ribosome flow models are studied. Ribosome flow models known from the literature are generalized by allowing an arbitrary directed network structure between compartments, and by
Mihály A Vághy, Gábor Szederkényi
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CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics [PDF]
Geoscientific Model Development, 2023 Microbial activity and chemical reactions in porous media depend on the local conditions at the pore scale and can involve complex feedback with fluid flow and mass transport.
H. Jung, H. Jung, H.-S. Song, C. Meile
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Quantitative Propagation of Chaos in a Bimolecular Chemical Reaction-Diffusion Model [PDF]
SIAM Journal on Mathematical Analysis, 2019 We study a stochastic system of $N$ interacting particles which models bimolecular chemical reaction-diffusion. In this model, each particle $i$ carries two attributes: the spatial location $X_t^i\in \mathbb{T}^d$, and the type $\Xi_t^i\in \{1,\cdots,n\}$
Tau Shean Lim, Yulong Lu, James Nolen
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