Real-time process analytics enable an insight into chemical processes and are essential to
implementing process optimization and control algorithms. However, the quantification of …

NEWSFRONT uch like technology-driven ex-M pert systems of the mid'80s, artificial neural
networks to day purport to be solutions in search of problems. Developers of neural nets are …

A novel time-varying neural network (TVNN) architecture incorporating time dependency
explicitly, proposed recently, for modeling nonlinear non-stationary dynamic systems is …

In this dissertation, we highlight recent developments in the application of machine learning
for molecular modeling and simulation. After giving a brief overview of the foundations …

This work introduces GraphormerMapper, a new algorithm for reaction atom-to-atom
mapping (AAM) based on a transformer neural network adopted for the direct processing of …

It is fundamental for science and technology to be able to predict chemical reactions and
their properties. To achieve such skills, it is important to develop good representations of …

In this review, we delve into the burgeoning utilization of intelligent algorithms within the
realms of chemistry and materials science. Starting with an elucidation of the fundamental …

Molecular dynamics with predefined functional forms is a popular technique for
understanding dynamical evolution of systems. The predefined functional forms impose …

The application of natural deep eutectic solvents (NADES) in the pharmaceutical,
agricultural, and food industries represents one of the fastest growing fields of green …

Machine learning (ML) based methods and tools have now firmly established themselves in
physical chemistry and in particular in theoretical and computational chemistry and in …