[HTML][HTML] Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data
T Kircher, FA Döppel, M Votsmeier - Chemical Engineering Journal, 2024 - Elsevier
The digitalization of chemical research and industry is vastly increasing the available data
for developing and parametrizing kinetic models. To exploit this data, machine learning …
for developing and parametrizing kinetic models. To exploit this data, machine learning …
[HTML][HTML] Robust mechanism discovery with atom conserving chemical reaction neural networks
FA Döppel, M Votsmeier - Proceedings of the Combustion Institute, 2024 - Elsevier
Chemical reaction neural networks (CRNNs) established as a useful tool for autonomous
mechanism discovery. While they encode some fundamental physical laws, mass-and atom …
mechanism discovery. While they encode some fundamental physical laws, mass-and atom …
The thermal decomposition mechanism of RDX/AP composites: ab initio neural network MD simulations
A neural network potential (NNP) is developed to investigate the decomposition mechanism
of RDX, AP, and their composites. Utilizing an ab initio dataset, the NNP is evaluated in …
of RDX, AP, and their composites. Utilizing an ab initio dataset, the NNP is evaluated in …
Kinetic models of HMX decomposition via chemical reaction neural network
Abstract 1, 3, 5, 7-Tetranitro-1, 3, 5, 7-tetrazocane (HMX) is commonly used in solid
propellants and explosives as energetic materials. The study of its kinetics and …
propellants and explosives as energetic materials. The study of its kinetics and …
Physics-Enhanced Machine Learning for Chemical Kinetics
FA Döppel - tuprints.ulb.tu-darmstadt.de
The energy transition and the transformation of the chemical industry are major efforts in
addressing the challenges of climate change. Both require the development of new and …
addressing the challenges of climate change. Both require the development of new and …