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 …

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 …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

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 …

The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the
framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and …

Nowadays, computer simulations have become a standard tool in essentially all fields of
chemistry, condensed matter physics, and materials science. In order to keep up with state …

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

Density functional theory offers a very accurate way of computing materials properties from
first principles. However, it is too expensive for modeling large-scale molecular systems …