Discovery of novel materials through machine learning.

Experimental exploration of new materials relies heavily on a laborious trial-and-error approach. In addition to substantial time and resource requirements, traditional experiments and computational modelling are typically limited in finding target materials within the enormous chemical space. Therefore, creating innovative techniques to expedite material discovery becomes essential.

Phase Transition in Silicon from Machine Learning Informed Metadynamics.

Investigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a large-size system.

Sustainable nano-sodium silicate and silver nitrate impregnated locally made ceramic filters for point-of-use water treatments in sub-Sahara African households.

The poor access to water quality for Nigerians has pushed for the designing of new trend silver nitrate impregnated locally made Point-Of-Use (POU) ceramic filters to enhance water purification efficiency for household use. This study utilized silver nitrate-molded ceramic filters prepared with Kaolin from Owode, silt soil, sodium silicate, sawdust, and distilled water in three varying proportions to ascertain pollution removal efficiencies. Heating was carried out by firing the filters at 900 °C and further preheating at 400 °C after dipping in silver nitrate solution.