Discovery of new materials is based on knowledge of the properties and the precognition of the impact of changes in chemical composition on these properties for future sustainable materials and devices with advanced performance. Data-driven materials research, i.e. , by artificial intelligence (AI) and machine learning, is a hot topic as these approaches can predict the structure–property relationship and may find use in elucidating the (thermophysical/chemical) properties–application (performance) relationship. This review focuses on the thermophysical properties, such as surface tension, viscosity, thermal and electrical conductivity, of liquid metals and alloys based on gallium and their influence on performance in various applications. The rise of Ga-based liquid metals stems from gallium’s fluidity and low toxicity, electrical and thermal conductivity, catalytic/reactive properties, and the ability to pattern and print it controllably. These properties are often exploited in stretchable electronics, soft sensors, e-skins, heat management, and reaction environment, but are not limited to these applications. In this regard, four case studies of applications are provided that necessitate careful design of alloy composition for high performance. The goal is to aid researchers identify appropriate alloy composition, which hinge on the desired application. The identified properties–application relationships may serve as starting points for machine learning approaches, reducing the need for time-consuming and labor-intensive experiments.
Gancarz et al. (Sat,) studied this question.