Chemical engineering is a data-rich subject. Practitioners collect and analyze data for
understanding flow patterns, developing empirical models, designing and optimizing …

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical
reaction pathways from time resolved species concentration data in a deterministic manner …

Knowledge in the chemical domain is often disseminated graphically via chemical reaction
schemes. The task of describing chemical transformations is greatly simplified by introducing …

In recent years, the prediction of quantum mechanical observables with machine learning
methods has become increasingly popular. Message-passing neural networks (MPNNs) …

In this Letter, we analyzed the inductive bias underlying complex free-energy landscapes
(FELs) and exploited it to train deep neural networks that yield reduced and clustered …

Artificial neural networks (ANN) provide a range of powerful new techniques for solving
problems in sensor data analysis, fault detection, process identification, and control and …

Kinetics of a reaction network that follows mass-action rate laws can be described with a
system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it …

Molecular simulations are widely applied in the study of chemical and bio-physical
problems. However, the accessible timescales of atomistic simulations are limited, and …

The digitalization of chemical research and industry is vastly increasing the available data
for developing and parametrizing kinetic models. To exploit this data, machine learning …

Discerning networks of many reactions among multiple interconverting species is
challenging. Here, we present a reaction network identification methodology. Our …