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Machine learning in process systems engineering: Challenges and opportunities
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
Digitization of chemical process flow diagrams using deep convolutional neural networks
Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis
Machine Learning in Chemical Engineering: A Perspective
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