Results 281 to 290 of about 669,188 (305)

Identifying Chemical Reaction Network Models

IFAC Proceedings Volumes, 2007
In this work, an automated chemical reaction network identification procedure using a genetic algorithm (GA) is introduced. The GA uses chemical species concentration data obtained from batch reactors during process experimentation to build ordinary differential equation (ODE) models that represent the chemical reactions occurring.
SC Burnham, M. Willis, Allen R. Wright
semanticscholar   +2 more sources

Kinetic models of HMX decomposition via chemical reaction neural network

Journal of Analytical and Applied Pyrolysis
Wei Sun, Yabei Xu, Xinzhe Chen, Qingzhao Chu, Dongping Chen   +4 more
semanticscholar   +2 more sources

Continuous Time Markov Chain Models for Chemical Reaction Networks [PDF]

, 2011
A reaction network is a chemical system involving multiple reactions and chemical species. The simplest stochastic models of such networks treat the system as a continuous time Markov chain with the state being the number of molecules of each species and with reactions modeled as possible transitions of the chain.
David F. Anderson, T. Kurtz
semanticscholar   +2 more sources

Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions.

Journal of Physical Chemistry A, 2023
Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of molecular property prediction (MPP) and chemical reaction prediction (CRP ...
Shilpa Shilpa, Gargee Kashyap, R. B. Sunoj   +2 more
semanticscholar   +1 more source

Optimization of the numerical treatment of the Darcy–Forchheimer flow of Ree–Eyring fluid with chemical reaction by using artificial neural networks

International Journal for Numerical Methods in Fluids, 2022
In this study, Darcy Forchheimer flow paradigm, which is a useful paradigm in fields such as petroleum engineering where high flow velocity effects are common, has been analyzed with artificial intelligence approach.
Anum Shafiq, A. B. Çolak, T. Sindhu
semanticscholar   +1 more source

Advancing molecular graphs with descriptors for the prediction of chemical reaction yields

Journal of Computational Chemistry, 2022
Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high‐yielding reactions and score synthesis routes, saving time and reagents.
Dzvenymyra Yarish, Sofiya Garkot, O. Grygorenko, D. Radchenko, Yurii S. Moroz, O. Gurbych   +5 more
semanticscholar   +1 more source

Dynamic Modeling of Chemical Reaction Systems with Neural Networks and Hybrid Models

Chemical Engineering & Technology, 1999
A common problem in kinetic modeling of complex chemical reactions is that a rigorous description of the reaction system, e.g., based on elementary reactions, is not possible. This is because either the reaction involves too many reactions and intermediates or the reaction mechanism is not known in sufficient detail. Alternative data-driven modeling, e.
H. Zander, R. Dittmeyer, J. Wagenhuber
semanticscholar   +2 more sources

Biochemical Reaction Network Modeling:  Predicting Metabolism of Organic Chemical Mixtures

Environmental Science & Technology, 2005
All organisms are exposed to multiple xenobiotics, through food, environmental contamination, and drugs. These xenobiotics often undergo biotransformation, a complex process that plays a critical role in xenobiotic elimination or bioactivation to toxic metabolites.
Arthur N, Mayeno, Raymond S H, Yang, Brad, Reisfeld   +2 more
openaire   +2 more sources

Cholesterol photo-oxidation: A chemical reaction network for kinetic modeling

Steroids, 2016
In this work we studied the effect of polyunsaturated fatty acids (PUFAs) methyl esters on cholesterol photo-induced oxidation. The oxidative routes were modeled with a chemical reaction network (CRN), which represents the first application of CRN to the oxidative degradation of a food-related lipid matrix.
C. Barnaba, RODRIGUEZ ESTRADA, MARIA TERESA, LERCKER, GIOVANNI, H. S. García, I. G. Medina Meza   +4 more
openaire   +2 more sources

Machine Learning for Chemical Reactions.

Chemical Reviews, 2021
Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be
M. Meuwly
semanticscholar   +1 more source