2021
DOI: 10.1039/d0sm02127j
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Determination of thermodynamic state variables of liquids from their microscopic structures using an artificial neural network

Abstract: In this work we implement a machine learning method based on an artificial neural network to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function.

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Cited by 7 publications
(3 citation statements)
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“…The mean free volume is defined by where the parameter B appears in the GvdW canonical equation of state and is defined as where g ( r ,ρ, T ) is the radial distribution function. Additionally, recent work has shown that Fourier series of RDFs can be used to develop ML models for atomization enthalpy, the prediction of dipole moments, , and the training of ANNs to successfully predict phase behavior of LJ fluids . These examples and many others in the literature present evidence for a strong relationship between self-diffusion and the local structure of a liquid, which can be represented through an RDF.…”
Section: Resultsmentioning
confidence: 99%
“…The mean free volume is defined by where the parameter B appears in the GvdW canonical equation of state and is defined as where g ( r ,ρ, T ) is the radial distribution function. Additionally, recent work has shown that Fourier series of RDFs can be used to develop ML models for atomization enthalpy, the prediction of dipole moments, , and the training of ANNs to successfully predict phase behavior of LJ fluids . These examples and many others in the literature present evidence for a strong relationship between self-diffusion and the local structure of a liquid, which can be represented through an RDF.…”
Section: Resultsmentioning
confidence: 99%
“…Therefore, we focus on the LJ fluid to make a database to train the ANN. The database is composed of several g(r) determined at different thermodynamic states that spans a wide region in the phase diagram [34].…”
Section: Molecular Model Of Simple Liquids and Training Data Setmentioning
confidence: 99%
“…[39,43] Thus, graph network can replace the existing molecular dynamics derivation process to study cluster dynamics specific processes through predictive methods. [44,45] This study aims to apply a novel machine learning approach to a classical physics problem. [46] We hope to obtain the final state of cluster motion at a specified time step using the predictive capability of graph network models with only the input of initial bit patterns.…”
Section: Introductionmentioning
confidence: 99%