The REC Group
Renewable Energy and Chemicals (REC) Research Group: Design of nanoscale materials with superior catalytic or electrocatalytic properties, essentially holds the key to success for developing renewable energy and chemicals. In our efforts, a bottom-up approach is applied, wherein quantum mechanical ab initio density functional theory (DFT) simulations of reactions occurring on the material surface are guiding the rational design of heterogeneous catalysts. The inherent design ideas vary and depend on the problem at hand. Overall, the ab initio level theoretical simulations provide us a mechanistic insight into the reaction, which in-turn offers us an opportunity to engineer the material itself. This is often implemented in experiments by changing the material surface, morphology and the characteristic length scales.
Read more at Reaction Chemistry & Engineering Blog
M. Ali Haider
Associate Professor, Chemical Engineering
B. Tech. IIT Guwahati, 2006
M. S. & Ph.D University of Virginia (UVA), 2006-2011
Postdoctoral Research Associate, NSF-CBiRC, UVA, 2011-2013
Visiting Scholar, CCEI, University of Delaware, 2016-17
Co-investigator: Energy Storage Platform on Batteries &
News
MAH is editing Perspectives in Modelling, Theory and Computational Catalysis 2022
Jayendran received Reba and Pranab Chatterjee Excellence Award
Dr. Ejaz Ahmad received Lovraj Kumar Memorial Trust Best PhD Dissertation of the Year 2019
MAH joins as an Associate Editor in Frontiers in Catalysis.
MAH selected as the member of The National Academy of Sciences, India
MAH has joined as an Associate Editor on Modelling, Theory and Computational Catalysis in Frontiers of Catalysis
MAH is chairing session on Elucidation of Active Sites and Reaction Mechanisms II in NAM-27 at New York.
MAH received Humboldt Research Fellowship for experienced researchers to visit TUM Catalysis Research Center
Reaction Chemistry & Engineering recognises MAH as Emerging Investigator
MAH selected for Dr. A.P.J Abdul Kalam HPC Award by Hewlett Packard Enterprise and Intel
JMCA paper selected for Editor's Choice Collection on Machine Learning for Materials Innovation