Deep neural networks (DNNs) represent promising approaches to molecular machine
learning (ML). However, their applicability remains limited to single-component materials …

The typically rugged nature of molecular free-energy landscapes can frustrate efficient
sampling of the thermodynamically relevant phase space due to the presence of high free …

Artificial neural networks (ANNs) have proven to be a valuable tool for the data-driven
construction of empirical models. The predictive capabilities of the ANN approximations are …

Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Approaches to the reaction prediction problems can be …

Analysing spectra from experimental characterization of materials is time consuming,
susceptible to distortions in data, requires specific domain knowledge, and may be …

With the rise of deep neural networks for quantum chemistry applications, there is a pressing
need for architectures that, beyond delivering accurate predictions of chemical properties …

The accurate prediction of tandem mass spectra from molecular structures has the potential
to unlock new metabolomic discoveries by augmenting the community's libraries of …

In a departure from conventional chemical approaches, data-driven models of chemical
reactions have recently been shown to be statistically successful using machine learning …

Although existing tensor completion methods have progressed in predicting two-and three-
dimensional data, they still struggle to capture nonlinearities and temporal dependencies in …

Computational workflows that combine molecular dynamics (MD) simulations and emerging
data-centric (DC) methods can accelerate the screening and analysis of solvent systems …