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. Recently, machine learning (ML) has emerged as a valuable tool for material discovery, garnering significant attention due to its remarkable advancements in prediction accuracy and time efficiency. This rapidly developing computational technique accelerates the search and optimization process and enables the prediction of material properties at a minimal computational cost, thereby facilitating the discovery of novel materials. We provide a comprehensive overview of recent studies on discovering new materials by predicting materials and their properties using ML techniques. Beginning with an introduction of the fundamental principles of ML methods, we subsequently examine the current research landscape on the applications of ML in predicting material properties that lead to the discovery of novel materials. Finally, we discuss challenges in employing ML within materials science, propose potential solutions, and outline future research directions.

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. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure. This approach has revealed the transition path and formation of polycrystalline silicon systems under specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics simulations in gaining insights into complex material behaviors in large-sized systems.

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. Silver nanoparticle and dissociated particle discharge from filter pot painted with 0.03 mg/g casein-covered nAg or AgNO3 were estimated as an element of pH (5-9), ionic strength (1-50mM), and cation species (Na+, Ca2+, Mg2+). Silver delivery was constrained by disintegration as Ag+ and resulting cation exchange measures, paying little heed to silver structure applied. Water analysis for both heavy metals (Pb and Cd) and microbial load (E. coli) evaluated, corroborate the maximum removal efficiency. It was observed that kaolin-sawdust with the Silver nitrate filters showed a constant and effective removal of both heavy metals and disinfection of microbial loads. The minimum flow rates observed were 4.97 mL/min for batch filter used for Iju River water sample one (AF1) and 4.98 mL/min for batch filter used for Iju River water sample two (AF2) having porosity 49.05% and 50.00%, whereas the 5 mL/min higher flow rate was used for batch filter from borehole water sample one (BF1) and batch filter used for well water sample two (CF2) with porosity of 50.00%. Significantly, the results obtained show that the filters are suitable for point-of-use application in both the urban and rural areas of developing countries such as Nigeria.