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 …

High-entropy ceramics with five or more cations have recently attracted significant attention
due to their superior properties for various structural and functional applications. Although …

Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …

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 …

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …

The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Deep learning is revolutionizing many areas of science and technology, especially image,
text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) …

In de novo drug design, computational strategies are used to generate novel molecules with
good affinity to the desired biological target. In this work, we show that recurrent neural …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …