Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered...

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Bibliographic Details
Published in:Computers (Basel) 2024-01, Vol.13 (1), p.2
Main Authors: Stavrogiannis, Christos, Sofos, Filippos, Sagri, Maria, Vavougios, Denis, Karakasidis, Theodoros E.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Argon
Data science
Datasets
Electric properties
Experiments
Heat conductivity
Krypton
Liquefied gases
Machine learning
Molecular dynamics
shear viscosity
Simulation
Simulation methods
Thermal conductivity
Transport properties
Viscosity
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Summary:Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.
ISSN:2073-431X
2073-431X
DOI:10.3390/computers13010002