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AI3SD Video: Generating a Machine-Learned Equation of State for Fluid Properties

AI3SD Video: Generating a Machine-Learned Equation of State for Fluid Properties
AI3SD Video: Generating a Machine-Learned Equation of State for Fluid Properties
Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental thermophysical data of fluids. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine- learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids. This work opens a pathway for employing classical molecular simulations with classical force fields as feeder of pseudo-data of fluids in the search for ML physical property prediction.
AI, AI3SD Event, Artificial Intelligence, Chemistry, Machine Intelligence, Machine Learning, ML, Property Prediction
Müller, Erich
637046f5-537c-4b1e-9273-3ce8ab1bb747
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Müller, Erich
637046f5-537c-4b1e-9273-3ce8ab1bb747
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Müller, Erich (2021) AI3SD Video: Generating a Machine-Learned Equation of State for Fluid Properties. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0073).

Record type: Conference or Workshop Item (Other)

Abstract

Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental thermophysical data of fluids. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine- learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids. This work opens a pathway for employing classical molecular simulations with classical force fields as feeder of pseudo-data of fluids in the search for ML physical property prediction.

Video
AI3SD-Winter-Seminar-Series-PropertyPrediction-ErichMuller (1) - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 17 March 2021
Additional Information: Erich A Müller currently works as a Professor of Thermodynamics at the Department of Chemical Engineering, Imperial College London. Erich does research in Molecular simulation, Chemical Engineering and Thermodynamics.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, Machine Intelligence, Machine Learning, ML, Property Prediction

Identifiers

Local EPrints ID: 448772
URI: http://eprints.soton.ac.uk/id/eprint/448772
PURE UUID: e69725bc-0d0f-4d67-98ac-b5d9add5330b
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 05 May 2021 16:33
Last modified: 06 May 2021 01:59

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Contributors

Author: Erich Müller
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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