Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Abstract

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.

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories

“…Frontiers in Nanotechnology frontiersin.org atomic trajectories, not macroscopic properties that can be statistically derived from trajectories, such as temperature, pressure, or transport properties such as thermal diffusivity or viscosity (Papastamatiou et al, 2022;Stavrogiannis et al, 2023;Sahputra et al, 2020). While it is possible that combining these networks as layers within more complex networks may enhance the performance in specific cases, our primary objective in this paper is to independently assess the advantages and disadvantages of each network type.…”