This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

The effective application of graphene and other 2D materials is strongly dependent on the
industrial-scale manufacturing of films and powders of appropriate morphology and quality …

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as
input computed or experimental materials data, ML algorithms predict the structural …

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 …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

Burning crop residues release large amounts of greenhouse gases, particulate matter,
carbon monoxide, etc. which influence a lot of environmental issues that are hazardous to all …

The culture of employing machine learning driven assistance and decision making is
currently adopted by a variety of industries. Artificial intelligence encompasses a wide range …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Hydrogen (H2) is a green and economical substitute to traditional fossil fuels due to zero
carbon emissions. Water splitting technology is developing at a rapid speed to sustainably …