Multi-mode operations are prevalent in the chemical industry. Various methods have been
proposed for monitoring multi-mode operations. Of these, AI-based approaches such as …

In times of educational disruption, significant advances in adopting digitalization strategies
have been accelerated. In this transformation climate, engineers should be adequately …

This work investigates the potential of hybrid modelling in the digitalization of the chemical
and biochemical industries. Hybrid modelling combines first-principles with data-driven …

In the present work, a multi-angle approach is adopted to develop two ML-QSPR models for
the prediction of the enthalpy of formation and the entropy of molecules, in their ideal gas …

This article investigates the applicability domain (AD) of machine learning (ML) models
trained on high-dimensional data, for the prediction of the ideal gas enthalpy of formation …

Abstract Structure-based virtual screening of binding of candidate drug molecules is a topic
of increasing interest in the discovery of small molecule drugs. As the same drug molecule …

With the advancement of deep learning (DL) methods in chemistry and materials science,
the interpretability of DL models has become a critical issue in elucidating quantitative …

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for
their success. One key aspect is the characterization of absorption, distribution, metabolism …

The complexity of coupled multivariate data in industrial settings often limits the
effectiveness of principal component analysis (PCA) in revealing patterns and structures in …

We review the utility and application of artificial intelligence (AI) and machine learning (ML)
at various process scales in this work, from molecules and reactions to materials to …