Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

The last two years have seen groundbreaking advances in natural language processing
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …

Physiochemical properties of pure components serve as the basis for the design and
simulation of chemical products and processes. Models based on the molecular structural …

Given the hesitance surrounding the direct implementation of black-box tools due to safety
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …

Recent years have seen a sudden spurt in the use of artificial intelligence (AI) methods for
computational reaction modeling and prediction. Given the diversity of the techniques, we …

Ionic liquids (ILs) could find use in almost every chemical process due to their wide spectrum
of unique properties. The crux of the matter lies in whether a task-specific IL selection from …