Different machine learning (ML) methods were explored for the prediction of self-diffusion in
Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the …

The ability to predict transport properties of liquids quickly and accurately will greatly
improve our understanding of fluid properties both in bulk and complex mixtures, as well as …

The self-diffusion coefficient of pure liquids, a fundamental transport property, is involved in
a wide range of applications. Many methods have been employed to study the self-diffusion …

Experimental data on diffusion in binary liquid mixtures at 298±1 K from the literature were
systematically consolidated and used to determine diffusion coefficients D∞ ij of solutes i at …

The molecular diffusion coefficient is fundamental to estimate dispersion coefficients,
convective mass transfer coefficients, etc. Since experimental diffusion data is scarce, there …

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion
constants for pure components in liquid, gas and super critical phases. The ANNs were …

Experimental diffusivities are scarcely available, though their knowledge is essential to
model rate-controlled processes. In this work various machine learning models to estimate …

In this article, at first, a quantitative structure–property relationship (QSPR) model for
estimation of limiting diffusion coefficients of hydrocarbon liquids, D AB 0, is developed …

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion
constants for individual components in binary fluid mixtures. The ANNs were tested on an …

Interplay of thermochemistry and Structural Chemistry, the journal (volume 23, 2012, issues
1–3) and the discipline | SpringerLink Skip to main content Advertisement SpringerLink Log in …