Dr. Saurabh Ghosh has joined SRM Research Institute and Department of Physics and Nanotechnology as Assistant Professor (Research) in June 2017. Before joining SRM, he worked as a postdoctoral researcher in USA from April 2011 to May 2017 (Cornell University, Ithaca, NY and Vanderbilt University, Nashville, TN /Oak Ridge National Laboratory, Nashville, TN). In SRM, he has formed and leading the ‘Functional Materials by Design’ (FMD) group.

The objective of FMD group is to design functional materials from first principles Density Functional Theory (DFT) calculations and Quantum Machine Learning (QML), guided by group theoretical techniques and supported by phenomenological model.

Using DFT, structural, electronic, magnetic and many other properties of a many electron system can be determined. With the advancement of modern supercomputing capabilities, DFT is not only successful in explaining experimental findings but also predicting materials with new functionalities. On the other hand machine learning (ML) refers to the automated detection of meaningful patterns in the data. It is a common tool to use in a task which requires information extraction from a large data set. The ML based algorithms are used in search engines, in credit card transitions, digital camera, flight scheduling and many more, in almost every accepts of our modern-day life. In this regard the application of ML and artificial intelligence (AI) based approach is rather new in materials science. The ML based approach can be used to predict new materials for enhanced functionality or to make strategies to enhance a particular property of a particular material.

In the context of designing new materials, understanding the ‘structure-property’ relation of a material is the key. The group is focused on designing functional materials by tailoring ‘structure-properties’ relation those are impactful in oxide electronics, spintronics, and energy research.

• Our aim is to combine quantum mechanics (QM) with machine learning (ML) to develop Quantum Machine Learning (QML) techniques for designing new materials for oxide electronics, spintronics, and energy storage.

• The FMD group is forming strong collaborations with other experimental and theory groups (in USA, Sweden, Italy, Japan and India) for research activities.

• The group is highly productive in publishing high quality Nature Indexed Journals (i.e., Phys. Rev. Lett., Nature Communication, Phys. Rev. B. etc.).