Artificial Intelligence (AI) technology has been developing for many years now; It can now be found not only in the field of technology but also in various places and industries.
Technology that works on the nanometer scale often includes complex systems that do not fit the various aspects of AI. However, there are some growing areas where AI can meet with nanotechnology. In addition to merging the two technologies, the combined work in nanotechnology and AI also enhances the study in each field, leading to all sorts of new tools for gaining insights and communication technologies.
Consider the following areas where AI and nanotechnology work together.
Microscope
Although atomic force microscopy (AFM) has seen significant progress in recent years, obtaining high-quality signals from these imaging devices can still be challenging. The main problem is that the tip-pattern interactions that rely on this microscope are complex, heterogeneous and therefore not easy to decipher. AI is very useful in solving these types of signal-related problems.
The AI approach known as Functional Recognition Imaging (FR-SPM) appears to solve this problem by directly detecting local actions from measured spectroscopic reactions. This process combines the use of artificial neural networks (ANNs) with Principal Component Analysis (PCA), which is used to sort the input data into the neural network.
In another imaging development, researchers at the University of Texas Rio Grande recently announced the creation of a microfluidic channel with a removable nanotextured surface that specifically binds to breast cancer cells. Once linked, you can capture and paint it. Imaging is segmented and combined with an AI algorithm that automatically determines whether or not cell cancer is based on historical current cell data. The novel imaging system contradicts historical models for the cells being evaluated in real-time.
Chemical Modeling
Algorithms are already being used to describe molecules and material frameworks to identify different properties and how they interact in different environments. Natural advances have led to the integration of AI and the use of complex machine learning algorithms.
From the modeling point of view, a variety of parameters must be correlated to create a dynamic description of an image or chemical system. As with some imaging techniques, AI can better analyze information and learn from the past to create a more accurate representation of the system under study. For example, AI can reduce the degree of error associated with the geometry or size of a system or cell. It is particularly useful for nanomaterials as many of the effects and phenomena found with materials such as graphene are often difficult to recreate.
The characterization of the structural properties of nanomaterials has also been addressed through the use of ANNs. For example, these algorithms have been used to determine the configuration of carbon nanotube structures by calculating structural properties such as alignment and curvature. Furthermore, many features of thin films have been widely solved by using AI, machine learning and neural networks.
Nano-Computing
Surprisingly, AI is also very useful for the future of nano computing, which is computing through nanoscale mechanisms. Currently, nano computing devices have many ways to perform a function and can cover anything from physical operations to computational methods. Due to the vast majority of these tools relying on complex physical systems to allow complex computational algorithms, machine learning approaches can be used to create novel information representations for a wide range of applications.
