Associate Professor
ME Department
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F32a_jain [at] iitb [dot] ac [dot] in
(+91) 22 2576 9363
Prof. Ankit Jain
- Materials Modeling and Characterization
- Micro/Nano Science and Engineering
Designing next-generation materials for energy applications.
1. Atomistic simulations and methods development for thermal and electrical transport.
2. Machine learning and high-throughput calculations for novel materials discovery.
3. Thermochemical and electrochemical reduction of Carbon Dioxide and Nitrogen gases into fuels (methanol, ethanol, etc) and ammonia.
Ph.D., Carnegie Mellon University, Pittsburgh, USA (2011-2015)
B.Tech, IIT Kanpur, India (2007-2011)
B.Tech, IIT Kanpur, India (2007-2011)
IBM SOSCIP, University of Toronto, Canada (2015-2017)
Stanford University, USA (2017-2018)
Denmark Technical University, Denmark (2018-2019)
Ankit Jain,,
Multichannel thermal transport in crystalline Tl3VSe4, Physical Review B, 2020,
Multichannel thermal transport in crystalline Tl3VSe4, Physical Review B, 2020,
Dhvaneel Visaria and Ankit Jain,,
https://www.me.iitb.ac.in/~a_jain/mypublications/APL_2020.pdf, Applied Physics Letters, 2020,
https://www.me.iitb.ac.in/~a_jain/mypublications/APL_2020.pdf, Applied Physics Letters, 2020,
R A Flores, C Paolucci, K Winther, A. Jain et al,
Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction, Chemistry of Materials, 2020,
Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction, Chemistry of Materials, 2020,
AJH McGaughey, A. Jain, HY Kim, B. Fu,
"Phonon properties and thermal conductivity from first principles, lattice dynamics, and the Boltzmann transport equation", Journal of Applied Physics , 2019,
"Phonon properties and thermal conductivity from first principles, lattice dynamics, and the Boltzmann transport equation", Journal of Applied Physics , 2019,
O. Voznny, L Levina, J Fan, M Askerka, A. Jain et al,
Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis, ACS Nano, 2019,
Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis, ACS Nano, 2019,