High-entropy nanoparticles have become a rapidly growing area of research in recent years.
Because of their multielemental compositions and unique high-entropy mixing states (ie …

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

High-entropy alloys are solid solutions of multiple principal elements that are capable of
reaching composition and property regimes inaccessible for dilute materials. Discovering …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

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 …

We formulate a materials design strategy combining a machine learning (ML) surrogate
model with experimental design algorithms to search for high entropy alloys (HEAs) with …

Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …

Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …

Rapidly discovering functional materials remains an open challenge because the traditional
trial-and-error methods are usually inefficient especially when thousands of candidates are …