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 …

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

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

There has been a growing effort to replace manual extraction of data from research papers
with automated data extraction based on natural language processing, language models …

Synthesis prediction is a key accelerator for the rapid design of advanced materials.
However, determining synthesis variables such as the choice of precursor materials is …

Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting
and sensitive analytical technique, has exerted high applicational value in a broad range of …

There currently exist no quantitative methods to determine the appropriate conditions for
solid-state synthesis. This not only hinders the experimental realization of novel materials …

Although various effective machine‐learning attempts have been made to investigate the
photoluminescence properties of graphene quantum dots (GQDs) or carbon dots, the …

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its
progress. Despite hidden variables and 'unknown unknowns' in datasets that may impede …

Data-driven synthesis planning with machine learning is a key step in the design and
discovery of novel inorganic compounds with desirable properties. Inorganic materials …