Research at IITM: CAMMD

Created by Divyaratna Joshi, Modified on Tue, 13 Jun 2023 at 04:17 AM by Divyaratna Joshi

The Centre for Atomistic Modelling and Materials Designing at IIT Madras focuses on the combined development of quantum many body theories, and computational first principle approaches with AI and machine learning integration to study and develop emerging materials. 

For fundamental analysis, the centre studies magnetic, superconductive and topological quantum properties of matter. The following avenues are of great interest to the program

  • Atomistic modeling through first principles: Through electronic structure calculations in many-body systems, the centre explores quantum phases of matter to build structure-property relationships between materials. 
  • AI guided topological insulator and superconductor design: Using the Monte Carlo Tree Search algorithm, different heterostructures between monolayer quantum spin hall insulators and s-wave superconductors will be sampled and optimal heterostructures will be identified.
  • Research and Design of 3D metal-ceramic interfaces: Despite hypothesized instabilities, the centre managed to interface metal-nitride mixing at thermodynamically favorable conditions to form interstitial solid solutions with Titanium Nitride. Chemically diffused structures like such exhibit interesting continuously varying elastic properties and crystal structures with the team studies.
  • Refractory High Entropy Alloys: Group IV, V, VI metals find applications in  strategic sectors owing to their high melting points and strength. By alloying these metals, the centre aims to reduce their oxygen absorption over time, making them more resistant to corrosion while maintaining their high melting point and strength.
  • Material Design through Machine learning: Efficient material production has two fundamental goals: rapid methodology for material property characterization and a design strategy to reduce candidate materials assessed to arrive at the target material. Through machine learning tools such as artificial neural networks and optimization algorithms, progress has been made especially in the context of biomolecules.

Through the broadly classified avenues mentioned above, the program aims to develop a materials database and launch open source software on machine learning integrated force field analysis, tight-binding Hamiltonian and experimental models. It would develop tools for visualization of materials in devices and interfaces. Through a Graphic User Interface (GUI), data mining and material design workflows would be generated to come up with novel materials with tailored properties.

Outreach to expose industries to the database and interface will ensure companies like LAM Research and Marmon Water Inc. reduce their experimental efforts and use the team’s expertise to accelerate development of new materials.

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