Deep Learning

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

Efficient Bayesian Uncertainty Estimation for nnU-Net

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its …

Flowsheet Recognition using Deep Convolutional Neural Networks

Flowsheets are the most important building blocks to define and communicate the structure of chemical processes. Gaining access to large data sets of machine-readable chemical flowsheets could significantly enhance process synthesis through …

HybridML: Open source platform for hybrid modeling

A tool for hybrid modeling.

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.