Nanoarchitectonics: the role of artificial intelligence in the design and application of nanoarchitectures
Source abstract
Abstract
Along with nanoscience advances, nanoarchitectonics have emerged as novel nanomaterials, with self-assembled arrangement of atoms and interesting properties. Hence, nanoarchitectonics could be applied to several fields, such as sensing, catalysis, water and wastewater treatment, fuel cells, solar energy storage, pharmaceuticals, and drug delivery. Some examples of nanoarchitectonics are self-assembled lipids, montmorillonite, sulfur-based surfactant/cyclodextrin complexes, hierarchical and conventional nanozeolites, metallic-organic frameworks, and smart polymers. Despite the features of nanoarchitectonics, the synthesis routes and underlying mechanisms are not fully understood, being characterized as a challenge in the field. In this context, artificial intelligence (AI) could be used to handle these challenges and to estimate physiochemical properties of nanoarchitectonics. Among the AI branches, machine learning (ML) methods, which include deep learning (DL), have been used either during the design of nanoarchitectonics or to predict specific thermal/physical/chemical properties of nanomaterials. Therefore, AI has been playing a great role in developing nanomaterials derived from nanoarchitectonics and, thus, with enhanced physiochemical properties. Then, AI could also be a promising tool during the scale-up modeling of nanoarchitectonics.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Photocatalysis Techniques · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (60)
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