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

The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …

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 …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …

Abstract Machine learning (ML) approach was applied for the prediction of biocrude yields
(BY) and higher heating values (HHV) from hydrothermal liquefaction (HTL) of wet biomass …

In heterogeneous catalysis, unravelling the distinct structural and compositional nature of
active sites provides a good platform in modulating important catalytic properties toward …

Recent literature suggests that the fields of machine learning (ML) and high-throughput
experimentation (HTE) have separately received considerable attention from chemists and …

Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …

The emerging H2 economy faces storage and transport challenges, and the use of ammonia
(NH3) as a CO x-free source of H2 via NH3 decomposition has recently attracted attention …