Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

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 …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Standfirst Over the last decade, we have witnessed the emergence of ever more machine
learning applications in all aspects of the chemical sciences. Here, we highlight specific …

Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry,
but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …

Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …