Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Rodolfo S.M. Freitas"'
Publikováno v:
Energy and AI, Vol 18, Iss , Pp 100454- (2024)
This study developed neural network potentials (NNPs) specifically tailored for pure ethylene and ethylene-ammonia blended systems for the first time. The NNPs were trained on a dataset generated from density functional theory (DFT) calculations, com
Externí odkaz:
https://doaj.org/article/dfce7c50cf304b22a578f2877304a336
Autor:
Rodolfo S.M. Freitas, Xi Jiang
Publikováno v:
Energy and AI, Vol 17, Iss , Pp 100385- (2024)
The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–proper
Externí odkaz:
https://doaj.org/article/d83ef0d0d0b0407fa77d64501bb78dd9
Autor:
Rodolfo S.M. Freitas, Ágatha P.F. Lima, Cheng Chen, Fernando A. Rochinha, Daniel Mira, Xi Jiang
Publikováno v:
Fuel. 329:125415
Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0766ec7227dc147b1c208d03ffa2ddae
https://doi.org/10.1016/j.fuel.2022.125415
https://doi.org/10.1016/j.fuel.2022.125415