The recent years have experienced a burgeoning growth in the development of statistical and machine learning within the domains of materials science and polymer chemistry. Interestingly, or rather unnoticeably, the concept of artificial intelligence was prevalent in the material science community for the past couple of decades. For instance, more than 15 years ago, a symposium proceeding conducted by the Materials Research Society had a session titled “Combinatorial and Artificial Intelligence Methods in Materials Science.” The trend has evolved recently with contemporary topics like high throughput screening, particle simulation accelerator, and using computational data sets to develop ground states.
The first question I asked myself is, why is this field proliferating now? Furthermore, if the area had been into practice 15 years ago, what happened to the techniques since then? Well, this somewhat resembles the rise and fall of the artificial intelligence, which generally has the crest and the trough, commonly termed as the ‘resurgence’ and ‘AI winters’ respectively.
The first spark was seen in 1956, when the context of artificial intelligence was created. Back then, the scientist didn’t know how to deal with the computational science. Moreover, there was no proper bridge that could link the experimental data with the theoretical data obtained from computational programming. The domain became more reinforced during the 1980s with the advent of powerful algorithms like backpropagation (for neural networks) and kernel methods (for classification). Now, with the integration of deep learning along with the growth in graphics processing units, the computational techniques have opened up a lot of avenues in the field of material sciences.
But, is the current technique enough to bridge the distance between the materials and the scientific community?
I guess, yes. The primary element which determines the robustness of an artificial intelligence processing and operation is the availability of large volumes of arranged data, which the literature terms as ‘libraries.’ These libraries enable us to use the machine learning fundamentals, but at the same time provide the scope to interpret them physically.
If harmonized and processed precisely, artificial intelligence not only allows us to accelerate our scientific developments but also the way particular research can be conducted. That is why you will find various recent articles that focus on ways to develop quicker routes to perform the same contemporary experiments. In this context, the Materials Genome Initiative, which was launched in 2011, had the sole intention to accelerate the material discovery process and to scale them up. The primary steps they used to establish the above goals were to apply the high throughput algorithm, both the theoretical and experimental modeling, to develop accessible libraries and repositories. Since then, the datasets have become a traditional solution to deal with complex problems in material sciences. The course of evolution eventually developed various datasets that contain thousands of experimental and theoretical data points including the Automatic Flow for Materials Discovery (AFLOWLIB), Joint Automated Repository for Various Integrated Simulations (JARVIS), density functional theory (DFT)), Polymer Genome, Citrination, and Materials Innovation Network.
The question remains- how exactly do these advanced techniques help us to develop a new perspective in material sciences? Well, let me give you an elementary example. Let say; I have developed a robust library with machine learning which hosts data for alloy designing. Once I know what kind of alloy to fabricate, I can set the parameters in the library to find the most optimized set of materials and operation tools which can fetch me the desired results in the least required time. Can we do the same using experimental and pure theoretical techniques? No, since most of the time shall be consumed while conducting trails from the vast set of the data. Moreover, these libraries can be extended to accelerate the synthesis optimization process, along with integrating train models to classify the crystal structures and defects. The most recent application involves the development of various de novo molecules for reinforced molecular designs for identifying materials with specific properties desired for various sensible operations.
As a concluding note, the availability of such databases and amalgamating them with theoretical and machine learning methods offer the potential to alter how materials science is approached substantially.
The article was originally published by The Times of India on 19–03–2020.
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