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High-throughput materials discovery

Obtaining materials with properties that are optimized for a particular application is at the core of materials science. However, materials development traditionally relies on time-consuming and expensive experimental work. High-throughput materials prediction has emerged as a tool for screening large databases of materials, to identify candidate matierials that may have optimized properties for subsequent experimental evaluation.

This collection of papers brings together recent works published in npj Computational Materials that contribute towards high-throughput materials discovery.

Articles

Experimental carrier concentration can serve as the basis for a model to understand and predict high performance thermoelectrics. Carrier concentration is instrumental in controlling properties. Despite significant experimental progress, establishing guidelines towards the desired performance through doping remains challenging. Now, a team from Northwestern University, Colorado School of Mines, and National Renewable Energy Laboratory in USA have predicted the dopability ranges of several diamond-like semiconductors, based on data from experimentally reported doping limits for 127 compounds. Several materials that combine simultaneously promising thermoelectric quality factor and complementary dopability are singled out. Apart from shedding light on what drives dopability in this family, the model also suggests that a number of less-studied compounds deserve more attention.

Article | Open Access | | npj Computational Materials

F-electron systems can possess interesting properties, and a database on these specific compounds could aid materials discovery. Here, Hasnain Hafiz at Northeastern University, and colleagues at Los Alamos National Lab, present the f-electron structure database. In contrast to other databases, computational data is generated with all electrons, resulting in a better description of these materials. Experimental information can sometimes miss essential data, but here an artificial neural network is used to correct this incompleteness, enabling correct determination (with 99.1% accuracy) of a crystal system. To verify the database, eight known double perovskites (AA′BB′CC′) were successfully found, and four unknown stable double perovskites were predicted. Moreover, electronic structure analysis tools in the database identified f-electron localization trends across the periodic table. This data-driven approach could drive the discovery of new f-electron materials, and lead to new applications.

Article | Open Access | | npj Computational Materials

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