A review and perspective on hybrid modeling methodologies

Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering

Mixed-integer optimisation of graph neural networks for computer-aided molecular design

Deep reinforcement learning for process design: Review and perspective

Machine learning in process systems engineering: Challenges and opportunities

Data-driven product-process optimization of N-isopropylacrylamide microgel flow-synthesis

Physical pooling functions in graph neural networks for molecular property prediction

Digitization of chemical process flow diagrams using deep convolutional neural networks

Toward automatic generation of control structures for process flow diagrams with large language models

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Geometry optimization of a continuous millireactor via CFD and Bayesian optimization

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

SFILES 2.0: an extended text-based flowsheet representation

Data augmentation for machine learning of chemical process flowsheets

Transfer learning for process design with reinforcement learning

An Educational Workshop for Effective PSE Course Development

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Graph machine learning for design of high-octane fuels