Akademik Veri Yönetim Sistemi
4.Uluslararası Mühendislik ve Doğa Bilimleri Çalışmaları Kongresi, Ankara, Türkiye, 24 - 25 Mayıs 2024, ss.586-599
This study focuses on the design and optimisation of
nanomaterials using artificial intelligence and deep learning techniques as
well as traditional materials science methods. Nanomaterials are materials
produced by nanotechnology and generally have dimensions in the nanometre
range. With the advancement of nanotechnology, nanoscale products are spreading
from electronics to healthcare products and pharmaceuticals, initiating a
radical change in the world order. The properties of these materials are determined
by many factors, ranging from their chemical composition to their crystal
structure and surface morphology. These properties are critical in determining
the performance of materials. Traditionally, the design and synthesis of
nanomaterials has been carried out through experimental studies. However, this
process is quite time-consuming and costly. With the introduction of artificial
intelligence and deep learning techniques, this process can be made more
efficient. Artificial intelligence and deep learning algorithms can identify
complex relationships and recognise patterns based on large amounts of data.
This enables them to analyse and optimise the relationships between
nanomaterial properties and synthesis conditions. This area of research has
great potential in materials science and can play an important role in the
discovery and design of next-generation materials. It can also contribute to
the dissemination of nanomaterials to a wider range of applications, enabling
the emergence of innovative solutions in many fields, from electronics to
biomedicine. To this end, the results of previous studies have been evaluated,
analysing the advantages of these techniques and the shortcomings of existing
methods.