Dr. Rohit Batra
Dr. Rohit Batra joined the Department of Metallurgical and Materials Engineering at IIT Madras as Assistant Professor in July 2022. Prior to this he had completed two Post-doctoral doctoral appointments, one at Center for Nanoscale Materials, Argonne National Lab, and the other at School of Materials Science and Engineering, Georgia Institute of Technology. He received his PhD in Materials Science and Engineering from University of Connecticut and B.Tech. in Metallurgical and Materials Engineering from IIT Roorkee. Before joining IIT Madras he also had a 4 month stint at Rivian Automotive LLC’s R&D lab for cell manufacturing and battery development.
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His research interest lies in the area of combining computational and data-driven methods to design new materials. He has expertise in first-principles electronic structure methods, molecular dynamics simulations, and many machine learning based techniques, particularly geared towards the general field of chemical and materials science.
THE TEAM
Abhijeet
Postdoc
Atomistic modelling and materials simulations using DFT and ML for Spin Hall Coupling studies on 2D materials
Vijith P
PhD candidate
Study and design of depolymerizable polymers using Monte Carlo tree search and ML based property prediction methods.
Talluri Yahwah Nissi
MS candidate
Using ML methods to formulate an analytical expression for self-assembly in peptides based on direct experimental data. This work is in collaboration with Argonne National Lab, USA.
Ujjwal Tripathi
M.Tech candidate
Design new polymer chemistries with high ionic conductivity for Li-ion batteries using machine learning
Devashish Kaushik
M.Tech candidate
Discovery of high temperature shape memory alloys using machine learning, DFT computations and experiments
Pragalbh Vashishtha
DD candidate
Defect detection in Additive Manufacturing (AM) process using image processing and AI, and optimization of AM processing parameters for targeted defect distribution.
Harisankar K
UG candidate
Prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys using Machine learning
Jeshlin
UG candidate
LLMs and Prompt Engineering for MultiModal Unstructured Data Mining of Shape Memory Alloys
Aravindan
UG candidate
Discovery of high temperature Shape Memory Alloys(SMAs) using machine learning and Large Language Models(LLMs)
Previous Members
Undergrads
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Yuvanandhan T
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Sabesh , currently employed at Quantiphi , Bangalore