Machine learning

Machine learning in process systems engineering: Challenges and opportunities

Machine learning in process systems engineering

A review and perspective on hybrid modeling methodologies

Hybrid modeling

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

Molecular design

Graph machine learning for design of high-octane fuels

Molecular design

Digitization of chemical process flow diagrams using deep convolutional neural networks

Flowsheet digitization

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Reinforcement learning for process design

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

Porperty prediction

HybridML: Open source platform for hybrid modeling

A tool for hybrid modeling.

Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis

Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis …

Machine learning in chemical engineering: A perspective

Discussion of perspecitves for future interdisciplinary research and transformation of chemical engineering by identifying challenges and formulation problems for machine learning.