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 …

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon
neutral future, and nanomaterials have played critical roles in advancing such technologies …

In the past decades, machine learning (ML) has impacted the field of electrocatalysis.
Modern researchers have begun to take advantage of ML‐based data‐driven techniques to …

With recent advancements in nanomedicines and their associated research with biological
fields, their translation into clinically-applicable products is still below promises. Quantum …

The global concerns of energy crisis and climate change, primarily caused by carbon
dioxide (CO 2), are of utmost importance. Recently, the electrocatalytic CO 2 reduction …

In the century-long development of fluidization technology, simulation methods have
evolved in response to scientific and engineering demands, which in turn have produced …

Machine learning (ML) with its indigenous predicting ability has been influential in the
current scientific world and has enabled a paradigm shift in the field of CO2 reduction …

Proton-exchange membrane water electrolysis is a promising technology for green
hydrogen production, but its widespread commercialization is hindered by the high cost and …

Autonomous atomistic computations are excellent tools to accelerate the development of
heterogeneous (electro-) catalysts. In this perspective, we critically review the achieved …

The waste management industry uses an increasing number of mathematical prediction
models to accurately forecast the behavior of organic pollutants during catalytic degradation …