Thermochemical treatment is a promising technique for biomass disposal and valorization.
Recently, machine learning (ML) has been extensively used to predict yields, compositions …

Machine learning has seen increasing implementation as a predictive tool in the chemical
and physical sciences in recent years. It offers a route to accelerate the process of scientific …

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 …

Molten salt–based processes and hydrofluxes are highly sensitive to mixture composition
and require knowledge of the combined melting point for successful materials syntheses. In …

Diffusion properties of bulk fluids have been predicted using empirical expressions and
machine learning (ML) models, suggesting that predictions of diffusion also should be …

Ionic liquids have many intriguing properties and widespread applications such as
separations and energy storage. However, ionic liquids are complex fluids and predicting …

Quantitative structure–property relationship (QSPR) model development driven by emerging
machine learning (ML) shows promise for accelerating design and preparation of jet fuels …

Diffusion-induced gas separation is crucial for industrial applications, while the
determination of specific conditions is still challenging. Here, molecular dynamics simulation …

Solvated organics in the ocean are present in relatively small concentrations but contribute
largely to ocean chemical diversity and complexity. Existing in the ocean as dissolved …

This paper presents an innovative approach to predicting thermophysical properties of
ethanol–octane blends by integrating molecular dynamics (MD) simulations with machine …