Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

In light of the pressing need for practical materials and molecular solutions to renewable
energy and health problems, to name just two examples, one wonders how to accelerate …

Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …

The development of machine-learned potentials for catalyst discovery has predominantly
been focused on very specific chemistries and material compositions. While they are …

The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on
many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its …

Deep learning models that leverage large datasets are often the state of the art for modelling
molecular properties. When the datasets are smaller (< 2000 molecules), it is not clear that …

It is insightful to report an estimator that describes how certain a model is in a prediction,
additionally to the prediction alone. For regression tasks, most approaches implement a …

Quantifying uncertainty in machine learning is important in new research areas with scarce
high-quality data. In this work, we develop an explainable uncertainty quantification method …

Accurate molecular property prediction, as one of the classical cheminformatics topics, plays
a prominent role in the fields of computer-aided drug design. For instance, property …

The availability and easy access of large-scale experimental and computational materials
data have enabled the emergence of accelerated development of algorithms and models for …