Artificial intelligence interventions in 2D MXenes-based photocatalytic applications

Abstract

Artificial Intelligence powered application have become the norms in day-to-day life. This has a tremendous role for material investigations catering diverse applications. Present day advanced materials as various MAX phases transformed into MXenes have immense applications for environmental use. MXenes have shown great potential in photocatalysis application targetingCO₂ reduction, H₂O₂ production, wastewater and dye treatment and nitrogen fixation. For an AI based implementation and model development, the basics of photon capture and charge transfer characteristics of photocatalytic materials, right from biological systems to organic/inorganic solar cells are crucial. This had been very thoroughly worked by compelling computational model and theories. The AI-ML based approaches have been instrumental in identification, screening, scrutiny of advanced materials, especially MXenes for varied applications via the supervised, unsupervised and reinforcement learning techniques. These exercises have provided the models that can be potentially more equipped for parallelly performing the classification and regression with a higher prediction accuracy. Use of advanced deep learning techniques have aided in establishing relation between structure-feature-properties and applications for MXenes based materials. Finally, a Criteria based AI aided Decision Support System is also discussed that prioritises environmentally sound and green MAX phase precursors for the development of photocatalytic materials. This will aid in developing technically feasible, economically viable and environmentally sustainable approaches for MXenes commercialization targeting environmentally friendly photocatalytic applications, thereby achieving sustainability development goals.