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 …

Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Compared to single atom catalysts (SACs), the introduction of dual atom catalysts (DACs)
has a significantly positive effect on improving the efficiency in the electrocatalytic nitrogen …

Owing to competitive behavior between oxidation products, complex oxidation commonly
exists for MAX phases applied at high temperatures. Two major challenges remain to …

Machine learning (ML) is causing profound changes to chemical research through its
powerful statistical and mathematical methodological capabilities. However, the nature of …

The development of high-throughput computation and materials databases has laid the
foundation for the emergence of data-driven machine learning methods in recent years …

Industrial waste pollution is a serious and systematic problem that harms the environment
and people. The development of cheap, simple, and efficient techniques to solve this …

The melting temperature is important for materials design because of its relationship with
thermal stability, synthesis, and processing conditions. Current empirical and computational …

Metal oxides are commonly used in methane activation and conversion, but usually suffer
from over-oxidation. The introduction of single atoms is an attractive way to overcome this …

Symbolic regression (SR), exploring mathematical expressions from a given data set to
construct an interpretable model, emerges as a powerful computational technique with the …