Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

Machine learning transforms the landscape of 2D materials
design,
particularly in accelerating discovery, optimization, and screening
processes. This review has delved into the historical and ongoing
integration of machine learning in 2D materials for electrochemical
energy applications, using the Knowledge Discovery in Databases (KDD)
approach to guide the research through data mining from the Scopus
database using analysis of citations, keywords, and trends. The topics
will first focus on a “macro” scope, where hundreds
of literature reports are computer analyzed for key insights, such
as year analysis, publication origin, and word co-occurrence using
heat maps and network graphs. Afterward, the focus will be narrowed
down into a more specific “micro” scope obtained from
the “macro” overview, which is intended to dive deep
into machine learning usage. From the gathered insights, this work
highlights how machine learning, density functional theory (DFT),
and traditional experimentation are jointly advancing the field of
materials science. Overall, the resulting review offers a comprehensive
analysis, touching on essential applications such as batteries, fuel
cells, supercapacitors, and synthesis processes while showcasing machine
learning techniques that enhance the identification of critical material
properties.