Machine Learning for Combustion Chemistry

Echekki, Tarek; Farooq, Aamir; Ihme, Matthias; Sarathy, S. Mani

doi:10.1007/978-3-031-16248-0_5

Cited by 6 publications

(4 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A chemical reaction neural net (CRNN) is a type of neural ODE model which incorporates fundamental physical laws, such as the law of mass action and the Arrhenius law, into its structure . CRNNs not only effectively capture experimental data but also enhance interpretability by facilitating the exploration of reaction pathways and kinetic parameters. The number of species and reactions, corresponding to the number of nodes in the input, hidden, and output layers, are indeed treated as CRNN hyper-parameters .…”

Section: Ann For Nanoscale Mixturesmentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

show abstract

Section: Ann For Nanoscale Mixturesmentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…In this work, we review PIML applied to fluid flows. Given the popularity of ML in contemporary research, numerous reviews on applying ML to science and engineering can be found from various perspectives including fluid dynamics [45,[57][58][59][60], combustion [61][62][63], environmental engineering [64,65], ordinary/partial-differential equations (ODEs/PDEs) [66] and specific PIML approaches [1,67]. In order to distinguish this review from existing literature, we aim to provide information and perspectives, specifically for addressing unique challenges and applications offered by employing PIML methods to fluid mechanics problems.…”

Section: Outlinementioning

confidence: 99%

A Review of Physics-Informed Machine Learning in Fluid Mechanics

et al. 2023

View full text Add to dashboard Cite

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

show abstract

“…Developing reliable combustion models is of great importance in the realm of combustion chemistry, which poses a challenge for theoretical chemists. − The reliability of a combustion model is largely associated with the accuracy of kinetic parameters employed, such as rate constants. ,, However, in many cases, accurate determination of thermal rate constants for combustion reactions remains very difficult, irrespective of experimental measurements and theoretical calculations …”

Section: Introductionmentioning

confidence: 99%

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Zhang,

Xia,

Song

et al. 2024

J. Phys. Chem. A

View full text Add to dashboard Cite

Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097−5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C−C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH 3 and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions.

show abstract

Machine Learning for Combustion Chemistry

Cited by 6 publications

References 74 publications

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

A Review of Physics-Informed Machine Learning in Fluid Mechanics

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Contact Info

Product

Resources

About