This is an AICTE approved Short Term Program
Department: Chemical Engineering
Intended audience: Industry participants, Faculty
About: Molecular modeling and simulation allow us to calculate macroscopic properties of a system from the knowledge of microscopic details such as forces of interactions. In other words, it is a mathematical way of mimicking the behavior of a collection of molecules. The simulation techniques can be broadly classified into two categories: Molecular Dynamics (MD) and Monte Carlo (MC). MD generates the trajectory (coordinates as a function of time) of a system of molecule as it goes from an initial state to the corresponding final state. From these trajectories, one can measure time-averages of properties of interest. Contrastingly, MC samples the phase space with an underlying distribution dictated by statistical mechanics, and one measures properties averaged over an ensemble of microstates of the system. Moreover, machine learning has remarkable ability to classify, recognize, and characterize complex patterns and trends in large data sets, and help in accelerating molecular simulation and improve the accuracy of molecular simulation-based property calculation. Integration of these two tools – molecular simulation and machine learning – can accelerate materials design and development.
Session dates: 21-03-2022 to 26-03-2022
Time: 9 AM to 5 PM IST
Last date of Registration: 18-03-2022
Profile of the Instructor(s)
Name: Prof. Ethayaraja Mani
Profile: Ethayaraja Mani is an associate professor of Chemical Engineering at Indian Institute of Technology Madras. He obtained a BTech degree in Chemical Engineering from Coimbatore Institute of Technology in 2003 and PhD from IIT-Bombay in 2009. He worked as a postdoc at Utrecht University, The Netherlands for two years (Aug 2008 – July 2010) and at The University of Amsterdam, The Netherlands for more than a year (Aug 2011 – Oct 2011). He joined IIT-Madras in Nov 2011 as an assistant professor and was promoted to associate professor in July 2017. His research interests are experimental and computation soft matter including Pickering emulsions, self-assembly, protein aggregation, nanoscience, active matter, polymer-nanoparticle composites, etc. He has taught the Molecular Simulation course for the last 10 years and held a short-term course for college teachers. He is the recipient of DAAD exchange fellowships to the University of Dusseldorf (2014) and Technical University of Berlin (2015). He is also the recipient of the Fulbright-Nehru fellowship in 2019 for visiting the University of Michigan. He was awarded the Shah-Schulman Best PhD thesis award (2009), Young Faculty Recognition Award (2014) and Amar Dye Chem Award (2016).
Name: Prof. Tarak K Patra
Profile: Tarak K Patra is an assistant professor of chemical engineering at the Indian Institute of Technology Madras. He received his B.Sc (Honors) in Physics from the University of Calcutta, Kolkata in 2004, and his PhD in chemical engineering from the Indian Institute of Technology Kanpur, Kanpur in 2014. He has carried out his post-doctoral studies at The University of Akron (2015-2017) and Argonne National Laboratory (2017-2019). His research expertise lies in the development of theory, simulation, and machine learning techniques for advancing the current understanding and design of polymers and soft matter. His current research focuses on polymer electrolytes, polymer nanocomposites and sequence-defined polymers. His research group activities can be found in his group webpage – https://home.iitm.ac.in/tpatra/
Eligibility & Fees
Eligibility requirement of participants: Faculty members from AICTE approved engineering colleges; industry participants
Maximum Number of participants that can be accommodated:
- Faculty – 30
- Industry participants – 20
- Faculty – ₹1,000
- Industry – ₹10,000
“The registration fee will be refunded for the first 30 faculty participants from AICTE colleges once they attend and complete the training program”.
Click here to download the Sponsorship Certificate format.
Click here to submit your sponsorship certificate and other details.
Criteria: Attending all the sessions and submitting assignments, if any.
Vandana Khanna –
Muruganandam Loganathan (verified owner) –
The course covers all areas in Molecular simulation and machine learnin. Case study was very useful.