| Journal: | Revista mexicana de física |
| Database: | PERIÓDICA |
| System number: | 000460633 |
| ISSN: | 0035-001X |
| Authors: | Torres Carbajal, A1 Que Salinas, U1 Ramírez González, P.E1 |
| Institutions: | 1Universidad Autónoma de San Luís Potosí, Instituto de Física, San Luis Potosí. México 2Universidad Nacional Autónoma de México, Instituto de Física, Ciudad de México. México |
| Year: | 2022 |
| Season: | Nov-Dic |
| Volumen: | 68 |
| Number: | 6 |
| Country: | México |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, teórico |
| English abstract | Abstact In this work an artificial neural network (ANN) was used to determine the pressure and internal energy equations of state of noble gases and some molecular liquids by predicting thermodynamic state variables like density and temperature encoded in the radial distribution function. The ANN is trained to predict the thermodynamic state variables using only the structural data. Then, predicted values are used to compute equations of state of real liquids such as argon, neon, krypton and xenon as well as some molecular liquids like nitrogen, carbon dioxide, methane and ethylene in the supercritical regime of each fluid. In order to assess the ANN predictions the relative percentage error with the exact values were determined, showing that its magnitude is less than 1%. Thus, the comparison between equations of state computed with the predicted variables and experimental results exhibits a very good agreement for most of the liquids studied here. Since our ANN implementation only requires the microscopic structure as an input, data incoming from experiments, theoretical frameworks or simulations are suitable to perform predictions of state variables and with that complement the thermodynamic characterisation of liquids through the determination of equations of state. Moreover, further improvements or extensions related with the microscopic structure database can be safely addressed without changing the neural network architecture presented here |
| Disciplines: | Física y astronomía |
| Keyword: | Física, Redes neuronales artificiales, Ecuación de estado, Líquidos moleculares |
| Keyword: | Physics, Artificial neural networks, Equation of state, Molecular liquids |
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