Results 21 to 30 of about 470,770 (313)

PNNARMA model: an alternative to phenomenological models in chemical reactors [PDF]

, 2001
This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose.
J.M. Zaldı́var, Zaldívar, J.M., Galván, Inés M., I.M. Galván, P. Isasi, Isasi, Pedro   +5 more
core   +1 more source

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

, 2022
Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data.
Samuel M., Blau, Shyam, Dwaraknath, Xiaowei, Xie, Kristin A., Persson, Mingjian, Wen   +4 more
core   +1 more source

Deterministic Models of the Simplest Chemical Reactions

Journal of Statistical Physics, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bunimovich, Leonid A., Demers, Mark
openaire   +3 more sources

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

, 2023
We develop Bayesian Chemical Reaction Neural Network (B-CRNN), a method to infer chemical reaction models and provide the associated uncertainty purely from data without prior knowledge of reaction templates.
Koenig, Benjamin C, Deng, Sili, Li, Qiaofeng, Chen, Huaibo   +3 more
core   +1 more source

Circuit Theory for Chemical Reaction Networks

Physical Review X, 2023
We lay the foundation of a circuit theory for chemical reaction networks. Chemical reactions are grouped into chemical modules solely characterized by their current-concentration characteristic—as electrical devices by their current-voltage (I-V) curve ...
Francesco Avanzini, Nahuel Freitas, Massimiliano Esposito   +2 more
doaj   +1 more source

Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias

, 2020
Organic synthesis remains a stumbling block in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes.
William, McCorkindale, Alpha, Lee, David Peter, Kovacs   +2 more
core   +1 more source

Modelling Chemical Reasoning to Predict Reactions

CoRR, 2016
17 pages, 8 ...
Marwin H. S. Segler, Mark P. Waller
openaire   +2 more sources

Suppressing Chaos for a Fractional-Order Chaotic Chemical Reaction Model via PDζ Controller

Journal of Mathematics, 2022
In this work, based on the earlier publications, we build a new fractional-order chemical reaction model. Computer simulations manifest that the fractional-order chemical reaction model presents chaotic behavior under a certain parameter condition.
Hui Wang
doaj   +1 more source

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

Journal of Cheminformatics, 2023
In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the ...
Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao, Hongming Chen   +8 more
doaj   +1 more source

Is EC class predictable from reaction mechanism?

, 2012
We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme ...
John BO Mitchell, Neetika Nath, Nath Neetika, Mitchell John BO, Nath, Neetika, Mitchell, John B. O.   +5 more
core   +1 more source