The main objective of IKST is to enhance collaboration among Indian and Korean scientists by leveraging the complementary strengths. IKST collaborates actively with various Institutes in India. In addition, it has a small internal R&D team that is involved in the broad area of computational materials science.
IKST team is involved in the extension of various models used in theoretical chemistry and in magnetic materials. Extended models for heterogeneous catalysis, role of surface magnetism in chemical reaction, spin-fluctuation-driven quantum phase transition in magnetic metals, population analysis with zero spilling factors are among the different areas we are working on.
These are materials designed for specific applications in energy storage and transport devices. Our group mostly focuses on designing catalysts for electrochemical reactions in these devices. We mostly work on alloy based or compound based catalyst.
These are materials with specific inherent properties which can be tailored suitably for various applications.
These are materials with wide applications in aerospace and automotive industries. Design of lightweight materials with high strength and ability to withstand extreme operating conditions is imperative for next generation of applications.
IKST team and its collaborators are involved in the development of simulation environments and source codes. The package CINEMAS has been developed by IKST which can be used for setting up manipulating visualizing and post-processing the density functional theory based calculations. To study the transport in semiconductor we have developed the AMMCR code which is interfaced with interfaces with Vienna ab-initio simulation package (VASP). It is written in C++ and is extremely fast. The work was done in collaboration with IIT-Bombay. To study the transition states and activation barriers for the chemical reactions and diffusion processes, we have developed in collaboration with Indian Institute of Science (IISc), a python based code, namely PASTA which is interfaced with multiple DFT codes. Further collaboration with IISc team and IKST resulted a post processing code for the widely used wannier90 package which can be employed for studying chemical bonding in covalently bonded materials..
Visit CINEMAS MicrositeA wealth of information is available in modern age in relatively unstructured ways such as scientific publications, web content, online documents, etc. Text mining and natural language processing (NLP) approaches are extremely useful in extracting valuable information from textual resources, in a sophisticated manner managing extracted information, and in building new knowledge through them. In recent years such methods have enabled automated text and table data mining across a wide range of scientific documents.Through collaboration with IITKGP, we are in the process of developing a suit
which can create auto-generated database of computational methods and the corresponding results from the online and offline scientific literature. The relevant scientific literature will be in the form of portable document format (PDF) or text.
Predicting different material properties using machine learning and deep learning methods has recently attracted considerable research interest. Recently proposed crystal graph convolutional neural network (CGCNN) is an efficient way of representing the crystalline materials through multigraphs or crystal graphs. By adapting Graph Convolution Neural Network (GCN) to these graphs one can generate the representation of each node and finally pooling layer is used to generate a single latent representation for the entire structure.
The CGCNN's main drawback in its current form is that large data set is required to train the properties. To overcome this issue by we have planned to develop a method which will use transfer learning approach within CGCNN framework. In addition, the integration of explicit features from certain online tools such as Matminer will increase the predictability. For such purpose we plan to develop a package in collaboration with IITKGP which is about to be scheduled.
Machine learning is a newly added tool in materials science that has the ability to significantly accelerate the discovery and design of materials with a large amount of data produced from experiments and high-throughput functional theory calculations. In recent years IKST researchers are focusing in studying in materials properties using machine learning based methods. Recently we have predicted the adsorption energies of mono-atomic and di-atomic gases on the surfaces of many transition metals (TMs) by using a machine learning approach. Our estimates of the adsorption energies are within within very root-mean-squared-error (RMSE) with less than 10 basic atomic features. Based on the important features of machine learning models, we have constructed a set of mathematical equations using a compressed sensing technique to calculate adsorption energy. Using similar approach we have predicted new III-V ternary semiconducting material with band gap close to the ideal value required for high efficient photo-voltaic applications.
