Prediction of equations of state of molecular liquids by an artificial neural network



Título del documento: Prediction of equations of state of molecular liquids by an artificial neural network
Revista: Revista mexicana de física
Base de datos: PERIÓDICA
Número de sistema: 000460633
ISSN: 0035-001X
Autores: 1
1
1
Instituciones: 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
Año:
Periodo: Nov-Dic
Volumen: 68
Número: 6
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, teórico
Resumen en inglés 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
Disciplinas: Física y astronomía
Palabras clave: Física,
Redes neuronales artificiales,
Ecuación de estado,
Líquidos moleculares
Keyword: Physics,
Artificial neural networks,
Equation of state,
Molecular liquids
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