Abstract Machine learning (ML) and deep learning (DL) possess excellent advantages in
data analysis (eg, feature extraction, clustering, classification, regression, image recognition …

Deep learning has become a powerful and frequently employed tool for the prediction of
molecular properties, thus creating a need for open-source and versatile software solutions …

The targeted removal of efficient but toxic corrosion inhibitors based on hexavalent
chromium has provided an impetus for discovery of new, more benign organic compounds …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

Knowledge of critical properties, such as critical temperature, pressure, density, as well as
acentric factor, is essential to calculate thermo-physical properties of chemical compounds …

The accuracy of thermochemical prediction methods is strongly dependent on the size of the
set of training data. Group additivity is an interpretable modeling strategy that can be …

Machine learning (ML) can provide decision-making advice for major challenges in science
and engineering, and its rapid development has led to advances in fields like chemistry & …

Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery
(LIB) development. The design of superior electrolytes requires extensive experimentation …

Although activated carbon's yield (quantity index) and BET area (quality index) are crucial to
its application, the two indexes must be accurately predicted. Herein, biomass compositions …

Chemical engineers heavily rely on precise knowledge of physicochemical properties to
model chemical processes. Despite the growing popularity of deep learning, it is only rarely …