The use of sophisticated machine learning (ML) models, such as graph neural networks
(GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly …

Machine learning (ML) approaches have demonstrated the ability to predict molecular
spectra at a fraction of the computational cost of traditional theoretical chemistry methods …

This work considers strategies to develop accurate and reliable graph neural networks
(GNNs) for molecular property predictions. Prediction performance of GNNs is highly …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Simulations of excited state properties, such as spectral functions, are often computationally
expensive and therefore not suitable for high-throughput modeling. As a proof of principle …

There is a lack of scalable quantitative measures of reactivity that cover the full range of
functional groups in organic chemistry, ranging from highly unreactive C–C bonds to highly …

Nowadays the development of new functional materials/chemical compounds using
machine learning (ML) techniques is a hot topic and includes several crucial steps, one of …

As machine learning (ML) methods continue to be applied to a broad scope of problems in
the physical sciences, uncertainty quantification is becoming correspondingly more …

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …

Deep neural networks have been increasingly used in various chemical fields. In the nature
of a data-driven approach, their performance strongly depends on data used in training …