Research on the state-of-the-art of efficient and ultra-clean ammonia combustion: From combustion kinetics to engine applications

Applied Energy, Journal Year: 2025, Volume and Issue: 391, P. 125886 - 125886

Published: April 15, 2025

Language: Английский

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Modeling of spray characteristics of alcohol fuels using response surface methodology and artificial neural networks

Fuel, Journal Year: 2025, Volume and Issue: 392, P. 134936 - 134936

Published: March 6, 2025

Language: Английский

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Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

International Journal of Quantum Chemistry, Journal Year: 2025, Volume and Issue: 125(7)

Published: March 19, 2025

ABSTRACT Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies Gibbs free energies, accelerating materials discovery optimizing reaction conditions in both academic industrial applications. This review investigates recent strides applying advanced machine techniques, including transfer learning, for accurately within complex chemical reactions. It thoroughly provides an extensive overview pivotal methods utilized this domain, sophisticated neural networks, Gaussian processes, symbolic regression. Furthermore, prominently highlights commonly adopted frameworks, such as Chemprop, SchNet, DeepMD, which have consistently demonstrated remarkable exceptional efficiency properties. Moreover, it carefully explores numerous influential studies that notably reported substantial successes, particularly focusing on predictive performance, diverse datasets, innovative model architectures profoundly contributed to enhancing methodologies. Ultimately, clearly underscores transformative potential significantly power intricate systems, bearing considerable implications cutting‐edge theoretical research practical

Language: Английский

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Study on the Effect of the Electron Density-Characterized Groups on the Nitrogen Transformation during Coal Pyrolysis

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: May 12, 2025

This paper clarifies the effects of functional groups on nitrogen migration during coal pyrolysis by utilizing density theory (DFT) calculations and support vector regression (SVR) modeling. First, study evidences enhanced electron-donating (EDGs) inhibition electron-withdrawing (EWGs). For example, for pyridine pyrolysis, inclusion -NH2 (EDG) is found to decrease endothermicity maximal barrier involved in HCN generation from 612.6 292.3 kJ/mol 624.2 296.0 kJ/mol, respectively. Second, DFT Rdkit descriptors are filtered constrain SVR model predict activation energy reaction energy. The results highlight importance S_type descriptor. Finally, TG-FTIR experiments using 2-pyridinecarboxylic acid 2-hydroxypyridine as test samples performed validate accelerated EDG group decelerated EWG, showing accordance with our All these findings will offer valuable insights understanding coal.

Language: Английский

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Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(21), P. 8131 - 8141

Published: Oct. 23, 2024

The prediction of the thermodynamic and kinetic properties elementary reactions has shown rapid improvement due to implementation deep learning (DL) methods. While various studies have reported success in predicting reaction properties, quantification uncertainty seldom been investigated, thus compromising confidence using these predicted practical applications. Here, we integrated graph convolutional neural networks (GCNN) with three techniques, including ensemble, Monte Carlo (MC)-dropout, evidential learning, provide insights into utility. ensemble model outperforms others accuracy shows highest reliability estimating across all property data sets. We also verified that showed a satisfactory capability recognizing epistemic aleatoric uncertainties. Additionally, adopted Tree Search method for extracting explainable substructures, providing chemical explanation DL corresponding Finally, demonstrate utility qualification applications, performed an uncertainty-guided calibration DL-constructed model, which achieved 25% higher hit ratio identifying dominant pathways compared without guidance.

Language: Английский

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Predicting Rate Constants of Hydrogen Abstraction Reactions between OH/HO₂ and Alkanes by Machine Learning Models

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

The hydrogen abstraction reactions by small radicals from fuel molecules play an important role in the oxidation of fuels. However, experimental measurements and/or theoretical calculations their rate constants under combustion conditions are very challenging due to high reactivity. Machine learning offers a promising approach predicting thermal constants. In this work, three machine methods, XGB, FNN, and XGB-FNN hybrid algorithms, were employed train predict between alkanes OH/HO

Language: Английский

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