Document
type: Class

Continuant
type: Class

Revista Mexicana de Fisica
type: Thing
type: Document
type: Information Content Entity
type: Collection
publication venue for: Prediction of equations of state of molecular liquids by an artificial neural network
type: Continuant
type: Journal
type: Entity
type: Generically Dependent Continuant
type: Periodical

Entity
type: Class

Prediction of equations of state of molecular liquids by an artificial neural network
artnum: 61702
type: Thing
estado: published
type: Document
most specific type: Article
type: Entity
crisid: 9925
related by: authorship_68905
type: Continuant
type: Article
accesstype: All Open Access, Gold
e iscopus: 2-s2.0-85141811352
date time value: article date 9925
has publication venue: Revista Mexicana de Fisica
most specific type: Document
freetext keyword: Artificial neural network; Equation of state; Molecular liquids
has funding vehicle: Estancias Posdoctorales Nacionales 1631, 257636, 422753/ 2020, CB-2015-01; Consejo Nacional de Ciencia y Tecnología, CONACYT
doi: 10.31349/RevMexFis.68.061702
abstract: 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%25. 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. © 2022, Revista Mexicana de Fisica. All Rights Reserved.
issue: 6
origen: scopus
volume: 68
related by: authorship_68906
type: Generically Dependent Continuant
type: Information Content Entity

Generically Dependent Continuant
type: Class

RDF description of Prediction of equations of state of molecular liquids by an artificial neural network - http://orbis.uaslp.mx/vivo/individual/scopus9924
rights: termsOfUse
date: 2026-08-27T12:55:14
publisher: vivo
type: Document

Article
type: Class

Estancias Posdoctorales Nacionales 1631, 257636, 422753/ 2020, CB-2015-01; Consejo Nacional de Ciencia y Tecnología, CONACYT
type: Relationship
type: BFO_0000020
type: Entity
type: Thing
type: Grant
type: Continuant
funding vehicle for: Prediction of equations of state of molecular liquids by an artificial neural network

Information Content Entity
type: Class