Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Exascale computers—supercomputers that can perform 1018 floating point operations per
second—started coming online in 2022: in the United States, Frontier launched as the first …

The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …

In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in
materials science to build predictive models and accelerate discovery. For selected …

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

Predicting, monitoring, and optimizing the performance and health of a battery system
entails a variety of complex variables as well as unpredictability in given conditions. Data …

High-level quantum mechanical (QM) calculations are indispensable for accurate
explanation of natural phenomena on the atomistic level. Their staggering computational …

SchNetPack is a versatile neural network toolbox that addresses both the requirements of
method development and the application of atomistic machine learning. Version 2.0 comes …