Workshop on Materials and Computational Chemistry Applications on HPC platform (9-11, October, 2023)


Monday, October 9, 2023 to Wednesday, October 11, 2023

The outcome of the projects under Materials and Computational Chemistry applications development under National Supercomputing Mission (NSM) are a set of codes (software) developed by the investigators to perform the computations to study properties of atoms, molecules, clusters, alloys, bio-molecules, and composite materials using high-performance computing (HPC).

The hand-holding workshop for the software developed under NSM (MCC) will be on the agenda during these three days. The sessions will have lectures and demonstrations of software delivered by the respective faculty leading the development. The limit on the number of participants (in person) in the workshop is thirty-five. The organizers will cover the expenses of the participants (Research fellows/PhD students/Post Doctoral fellows) for their stay and travel (III-AC by rail). We encourage other interested participants to join the workshop proceedings online.

A summary of the software/tools follows:

GUI for home-grown quantum chemistry code (MTASpec):
It is based on the fragmentation-based molecular tailoring approach, enabling economic and efficient ab initio computation of the vibrational spectra of large molecules and clusters. The code uses the Gaussian suite at the back end and is fully automated and user-friendly for use on the Linux platform. [Prof. Shridhar Gadre, Savitribai Pule Pune University]

Linear Scaling DFT (AMDKIIT):
It is a hybrid density functional theory(DFT)--based molecular dynamics program for performing plane-wave and pseudo-potential-based DFT calculations. It has various options for using the publicly available pseudo-potentials apart from using a wide variety of density functionals available through libXC. The code can perform wave function and geometry optimization. This program can potentially use future exascale computing systems. [Prof. Nisanth Nair, IIT Kanpur]

Multi-Reference Methods with hybrid QM/MM approaches (ANN-CI):
The quantum mechanical calculation of properties in complex systems using these methods based on depicting the charge transfer states and excited states, besides describing the interaction with the environment, using the polarizable force fields, is computationally expensive. The supervised machine learning for computing these fields is as accurate as QM methods and considerably cheaper computationally. Computational chemistry, augmented by machine learning, is a powerful tool for studying complex biological systems. [Prof. Debashree Ghosh, IACS, Kolkatha]

Excited state dynamics toolkit (LITESOPH):
Layer Integrated Toolkit and Engine for Simulations of Photo-induced phenomena (LITESOPH) is a comprehensive toolkit to launch, monitor, manage, and analyze large-scale simulations of photo-induced phenomena in a high-performance computing (HPC) environment. Its design serves the needs of computational researchers interested in solar energy conversion applications (photovoltaics, water-splitting catalysts, solar fuels, etc.), optoelectronic materials, photochemistry, and photobiology. The toolkit consists of several Python-based layers driven by popular and open-source TDDFT codes like OCTOPUS, GPAW, and NWChem. [Prof. Varadharajan Srinivasan, IISER Bhopal]

Multiscale Microstructure Simulation and Modelling (µ2Mech)
It is a multiscale modeling combining atomistic and phase-field simulations for microstructure modeling during solid-state phase transformations. It provides microstructure-based prediction of effective elastic properties using the finite element method. It includes an interface between the microstructure prediction and the property prediction module. [Prof. Rajdip Mukherjee, IIT Kanpur]

Important Dates:
Start of registration: 9, September, 2023
Last date for registration: 18, September, 2023
Announcement of participants: 19, September, 2023
Workshop: 9-11, October, 2023

Registration link:

Please mail to: for any queries related to the workshop.


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