Breakthrough in Electronic Materials and Chemicals Market: New Accessible AI Tools Can Speed Up Discovery of Functional Electronic Materials

• Analysis
• 12-August-2021

The discovery of designing new materials that have tunable electronic properties is essential as it is the primary key to producing microelectronic devices faster while also being energy efficient could design new computer architectures.
 
Recently a research team has made use of AI (Artificial Intelligence) to build a collection of tools that are free, new, and user friendly. The innovation would empower scientists to discover and study those materials that showcase an MIT (Metal-Insulator Transition) at a much faster rate. Further, it would also allow the identification of new features that can describe a class of such materials. The new tool kit would be a great contribution to the Electronic Materials and Chemicals Market and could lead to the sector’s advancement as it would empower scientists to increase the rate at which they discover materials with MIT. 
 
The team stated that one of the primary elements of their model ad tools therein is the accessibility they provide to a large audience. This is because engineers and scientists do not need the knowledge of machine learning to use them. 
 
The researchers combined their knowledge of MIT materials with their understanding of NLP (Natural language processing). They looked through the entire existing literature on the concept and identified the 60 know MIT compounds along with 300 materials that have similar chemical composition; however, they do not demonstrate MIT. The team brought forth the resulting materials and features that they found to be relevant and will make them accessible to the scientist in a freely accessible database for public use. 
 
The researchers, in order to build the database, made use of machine-learning tools and identified essentials features characterizing these materials. Through experiments, the team confirmed the importance of specific features such as electrostatic repulsion among them, distances between transition metal ions, and the accuracy of the model. Moreover, they also recognized new features that were previously underappreciated, like the different sizes of atoms from each other or measures of the amount at which inter-atom bonds are ionic or covalent. All the features were considered to be critical for the development of a reliable machine learning model addressing MIT materials and can be packaged easily into an openly accessible format. 
 
The innovative free tool would allow any person to quickly procure probabilistic estimates like whether the materials being studied by them is metal, metal-insulator transition compound, or an insulator. Thus, becoming a great benefit for researchers and reducing their unnecessary time for research.