The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing
centers, and open materials databases have transformed the accessibility of computational …

The need for improved functionalities in extreme environments is fuelling interest in high-
entropy ceramics,–. Except for the computational discovery of high-entropy carbides …

The nitrogen reduction reaction (NRR) is a renewable alternative to the energy-and CO2-
intensive Haber–Bosch NH3 synthesis process but is severely limited by the low activity and …

Despite advances in machine learning for accurately predicting material properties,
forecasting the performance of thermosetting polymers remains a challenge due to the …

Machine learning (ML) is believed to have enabled a paradigm shift in materials research,
and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of …

Electrocatalytic nitrate to ammonia conversion is a key reaction for energy and
environmental sustainability. This reaction involves complex multi electron and proton …

Transition metal dichalcogenides exhibit phase transitions through atomic migration when
triggered by various stimuli, such as strain, doping, and annealing. However, since …

Traditional electrical power grids have long suffered from operational unreliability, instability,
inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the …

A grand challenge of materials science is predicting synthesis pathways for novel
compounds. Data-driven approaches have made significant progress in predicting a …