One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

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

Retrosynthetic route planning can be considered a rule-based reasoning procedure. The
possibilities for each transformation are generated based on collected reaction rules, and …

Automatic design of organic materials requires black-box optimization in a vast chemical
space. In conventional molecular design algorithms, a molecule is built as a combination of …

With the notable development of radiative cooling research and the continuous improvement
of cooling effect, its practicability and application range have been widely concerned …

Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …

Automated materials design with machine learning is increasingly common in recent years.
Theoretically, it is characterized as black-box optimization in the space of candidate …

The prediction of stable crystal structures is an important part of designing solid-state
crystalline materials with desired properties. Recent advances in structural feature …

Materials development often confronts a dilemma as it needs to satisfy multifunctional, often
conflicting, demands. For example, thermoelectric conversion requires high electrical …

In recent years,(big) data science has emerged as the “fourth paradigm” in physical science
research. Data-driven techniques, eg machine learning, are advantageous in dealing with …