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

Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

Graph neural networks (GNN) have been shown to provide substantial performance
improvements for atomistic material representation and modeling compared with descriptor …

The design of molecular catalysts typically involves reconciling multiple conflicting property
requirements, largely relying on human intuition and local structural searches. However, the …

Abstract PubChem (https://pubchem. ncbi. nlm. nih. gov) is a popular chemical information
resource that serves a wide range of use cases. In the past two years, a number of changes …

In recent years, due to its great promise in boosting the energy density of lithium batteries for
future energy storage, research on the Li metal anode, as an alternative to the graphite …

There is a perceived dichotomy between structure-based and descriptor-based molecular
representations used for predictive chemistry tasks. Here, we study the performance …

Quantum and classical reaction rate constant calculations come at the cost of exploring
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …

The great tunability of the properties of halide perovskites presents new opportunities for
optoelectronic applications as well as significant challenges associated with exploring …

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the
chemical evolution and associated functionality of the resulting surface film is one of the …