Bench Talk

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Artificial intelligence (AI) is starting to be used more and more across several industries, as it helps to make processes far more efficient. AI methods are becoming increasingly important as well as we move toward Industry 4.0 and more automated industrial systems. Among the many AI methods, machine learning (ML) has become one of the most popular. In addition to the various manufacturing, monitoring, computing, and process industries that are adopting machine learning algorithms, machine learning methods are being used in conjunction with nanotechnology—an area that is not as well documented as some others.

Given that both AI and nanotechnology industries are still in relative infancy compared to some industries that have been going for well over a century, some challenges are still associated with both of these industries. Naturally, combining these two highly advanced industries also have challenges. They range from data methods being faster than the physical experiments, to a lack of effective communication between the relevant researchers in each industry about what each industry needs from the other—and how best to exploit both areas to produce optimized results.

Nevertheless, with challenges come opportunities, and many opportunities are available at the interface of AI and nanotechnology. So, despite a few challenges, these could be overcome with relative ease, so let’s look at some of the key emerging areas where machine-learning methods and nanotechnology are combining to produce effective results. These areas include analyzing large data sets, designing and discovering new nanomaterials, and developing more efficient hardware to power machine-learning algorithms.

Analyzing Large Data Sets

Analyzing, optimizing, and picking trends in large data sets is at the core of machine-learning methods, and this is something that can be applied to nanomaterials. This is developed in several ways.

• The first way is to analyze the data from various characterization instruments, such as when using spectroscopy methods and electron microscopy methods to characterize the nanomaterials’ properties. A combination of artificial neural networks (ANNs) and convolutional neural networks (CNNs) have been used in conjunction with characterization instruments.

On the one hand, machine learning is used with spectroscopy to indicate very small changes in data sets that might not otherwise be noticeable. These small changes can actually correlate to changes in the chemical structure and morphology of the material being analyzed, which are two factors that can change the properties of the nanomaterial. So, the ability to identify such small changes is quite important.

On the other hand, machine learning is used with microscopy methods—specifically electron microscopes used to analyze nanomaterials, but machine-learning methods have also been used with optical microscopes for other types of materials. In this area, the output is an image with spatial correlations, and machine-learning methods can be used to detect small deviations from the norm, leading to a more accurate analysis of the material. This can also be applied beyond pure nanomaterial analysis to analyze the spatial features of biological features—such as using the shape and size of cells to determine which ones are cancerous. Although this is not strictly nanotechnology, many of the applied approaches to achieve to analyze these cells rely on nanomedicine approaches, so it is a closely related area.

• The second key way is to separate data sets of a characterization instrument. Many analytical methods tend to compress data, and machine learning can separate the different data signals from the analysis. This is important because data compression can lead to mixed signals developing, which can affect the results. So, machine learning can essentially be employed to nanomaterial analysis methods to act as a means of quality control and provide more accurate data outputs from the original data sets.

Designing and Discovering New Nanomaterials

One scientific area that has gathered a lot of interest in recent years is the ability to optimally design nanomaterials—as well as many other materials and chemicals—and find routes to produce new materials that might be better than the status quo. The need has been so great that it has led to many computational/theoretical fields developing—such as computational chemistry and biology—which has become more and more prevalent with the sharp increase in computing power over the last decade or so.

Machine-learning methods are being employed because nanomaterials’ properties are much harder to predict than other materials because of quantum effects coming into play at such small scales. ANNs, deep neural networks (DNNs), and generative adversarial networks (GANs) have been utilized to analyze and optimize the many different parameters and properties possible at the nanoscale. These outputs can be rationalized and used to build up the best-possible solution for designing a new nanomaterial or the best way of optimizing an already existing nanomaterial. It’s like an advanced version of computational chemistry/biology that can be used with materials that exhibit unique properties and phenomena. Such methods have been used to design and optimize a range of nanomaterials, including 2D materials, 2D material heterostructures, nanoscale catalysts, nanophotonic materials, and 1D materials, to name a few.

More Efficient Hardware

Although the above areas have focused on what machine learning can do for nanotechnology, this section is devoted to what nanotechnology can do for machine learning. The advanced nanofabrication and nanopatterning methods nowadays can create highly efficient and small computer hardware devices. These advanced computing components can then be utilized to provide more computational power, which can be used to power and sustain machine-learning algorithms.

Besides being able to pattern existing nanoscale materials to make them more efficient, creating nanoelectronic devices enables traditional components to be much smaller, meaning that more components can be fabricated in a given area. A perfect example of this is nanoscale transistors’ development, as many more transistors can be fabricated onto a chip/hardware than with other bulkier transistors, enabling the speed and efficiency to be increased.

The use of nanomaterials has also led to the development of novel transistor-based devices, such as memristors, which can “act like a brain” and store information—which remains stored when the power is removed. The ability to produce faster hardware and advanced components that can facilitate the “brain-like” behavior of machine learning and other AI algorithms will help to further implement machine-learning algorithms to more applications and industrial sectors.

Conclusion

Despite the challenges within both industries and combining two high-tech industries together, many opportunities are possible by combining nanotechnology with AI methodologies, many of which are starting to gain interest. Machine-learning methods can be employed to better analyze nanomaterials and nanoscale biological materials, and aid in the effort to find new materials and the best routes to design nanomaterials optimally. Nanotechnology can also give back as well by providing more efficient hardware to power machine-learning algorithms. Overall, it’s still a developing area, but it’s a cross-over area with potential on many fronts.