Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

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 …

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 …

The modular nature of metal–organic frameworks (MOFs) enables synthetic control over
their physical and chemical properties, but it can be difficult to know which MOFs would be …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful
tool for structural and functional imaging of materials on the atomic level. Driven by …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …