Prof. (Dr) Sukhendu Samajdar expressed his sincere thanks to the Vice Chancellor, distinguinshed panelists and respected participants. He acknowledged that under the inspiring leadership of MAKAUT's Hon'ble Vice Chancellor it has been possible to encourage and educate the student community. He also said that MAKAUT cares for 150000 students through its in-house courses and 200 affiliated colleges. He then gave a brief but insightful account of materials informatucs. He said, " We nees knowledge to tailor materials for new technologies and to do it Computational Material Science is needed, whose goal is to accelerate the design of the materials. There are many established tools like Density Function Theory, Phase Field , Finite Element Analysis, Computational Thermodynamics, etc. But the emerging field is the Data Driven Models.
We need to have an idea of the things around us, what they are made up of and what else they can be made up with. The interconnection of knowledge from various fields is very important. Links with other disciplines will help to integrate information and realize the importance of Material Science. The inception of Materials Informatics is from the 1990s. A pioneering example in this field is the Ashby Plot designed by Michael Ashby from University of Cambridge. Ashby Plot compressed a lot of plots required for engineering design, that is a lot of useful information is condensed to cater the
needs of engineering materials. Subsequently in 2008 The National Research Council articulated the idea of Integrated Computational materials engineering (ICME), which is the integration of materials information captured in computational tools with engineering product performance analysus and manufacturing-process simulation. The developed committee curated data sets, structure-property models, processing-structure relationships, physical properties, and thermodynamic, kinetic and structural information. In 2011 the
Obama Government in The United States launched the Materials Genome Initiative, with the objective of squeezing the time span of a material from its development to deployment like an accordian. In this context the 3 pillars were: Computatuional tools, Experimental tools and Digital Data running in harmony. We have huge data from variety of sources such as simulation, experiment etc. This data is processed and then made available. for this process there are two approaches to analyze data
To summarize it can be said that materals scientists are constantly striving to advance their ability to understand, predict and improve material properties. Due to high cost of traditional trial and error methods in isolation, material scientists are increasingly relying on simulationa and modeling methods. Materials informatics is a resultng branch of materal science that utilizes high throughput computation to analyze large databases of materials properties to gain unique insights and discover materials in real time. More recenty data driven methods such as machine learning havs been adopted in this field to study the wealth of existing experimental and computational data, leading to a paradigm shift in the way naterial science research is conducted."
Prof. (Dr) Subhabrata Datta expressed his sincere gratitude to MAKAUT family for providing the
opportunity to share his thoughts in this domain of materials informatics. He said, " There are several important properties of materials. The different properties are required for different materials to be engineered. For a particular application more than one property is required to make the material perfect. The performance depends on a combination of properties and the best material is selected by maximizing one or more 'performance indices'. As civilization progressed, the materials being used also changed. In the early ages of civilization metals and alloys were extensively used. Now with the dixcovery of new materials their uses are decreasing day by day.
This will give rise to the desired properties, which in turn will help the material to perform better.
In the physics driven approach all the tools simulate the microstructures, after which the properties can be mapped using different methods like property prediction.
There may be many attributes of a material that may not be revealed, which may lead to uncertainty and imprecessions. This is why data driven models are required. But it cannot be a competitor to physics based models. It can only support the process of material discovery.
The experimantal equipments have improved a lot in the past few years. So generation of data is nit that difficult, if the facility is available. For example, Atom probe tomography(APT) is a new technique. It can map atom in a 3D space for a small specimen like a tip of a needle and generate few terabytes of data.
The technique of conventional science is to reduce the problem until it can be expressed using rigorous mathematical theory and then to do simplified experiments to validate the theory. Data-driven modeling faces the problem at the level of complexity posed. It begins with wide consultation to identify all the relevant issues. Methods are then assembled and developed. In this way, the technological goal is achieved, and problems are identified which in the longer term need to be resolved using the scientific approach.
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