This monograph presents recent advances in neural network (NN) approaches and
applications to chemical reaction dynamics. Topics covered include:(i) the development of …

Opening Up the Black Box of Artificial Neural Networks Page 1 Opening Up the Black Box of
Artifical Neural Networks M. T. Spirting The University of Tennessee, Knoxville, TN 37996-1600 …

In this work, we detail an automated reaction network hypothesis generation protocol for
processes involving complex feedstocks where information about the species and reactions …

Chemical reaction neural network (CRNN), a recently developed tool for autonomous
discovery of reaction models, has been successfully demonstrated on a variety of chemical …

Deep neural networks have evolved into a powerful tool applicable for a wide range of
problems. However, a clear understanding of their internal mechanism has not been …

The design of chemical formulations is a challenging, high-dimensional problem. In typical
formulations, tens of thousands of ingredients are available for use, yet only a tiny fraction …

Deep learning has been shown to learn efficient representations for structured data such as
images, text, or audio. In this chapter, we present neural network architectures that are able …

Abstract Machine learning and artificial intelligence are increasingly gaining in prominence
through image analysis, language processing, and automation, to name a few applications …

A collection of new approaches to building and training neural networks, collectively referred
to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to …

The estimation of chemical reaction properties such as activation energies, rates, or yields is
a central topic of computational chemistry. In contrast to molecular properties, where …