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Research interests of Selva Chandrasekaran Selvaraj are Computational Materials Science, Deep Quantum Chemical Simulation and Materials Informatics, Machine learning Force Fields Development, Quantum and molecular mechanics simulations, machine-learning methods, and Design and development of high-performance green materials for energy conversion, storage, and utilization
Figure. (a) Simulation methodology of large-scale DLMD that involves the following steps: 1) Generating training data of atomic coordinates, forces, and energies (Ri, Ei, and Fi) from AIMD simulations, 2) Converting AIMD data to descriptors Di, 3) training the machine-learning model with deep neural networks, and finally 4) running large-scale DLMD with LAMMPS. (b-g) Comparing force and energy accuracies and computational time between AIMD and DLMD. Figure 1 (b-g) are grabbed from our work https://iopscience.iop.org/article/10.1149/1945-7111/ad4ac9/meta.
Integrating multi-scale simulation and machine learning tools for rapid screening of superionic conductors and characterization of hybrid Li/Na-ion batteries
Integrating multi-scale simulation and machine learning tools for rapid screening of superionic conductors and characterization of hybrid Li/Na-ion batteries
Large-scale simulations using machine-learned force fields:
Large-scale simulations using machine-learned force fields:
Defect passivation mechanisms:
Defect passivation mechanisms:
Passivation mechanism in CdTe solar cells: The hybrid role of Se
https://aip.scitation.org/doi/full/10.1063/5.0058290
2D and 3D Semiconductors:
2D and 3D Semiconductors:
Controlling of spin and charge can be achieved in two dimensional periodic structures due to their tunable electronic properties. From III-IV compounds to monolayers of C, Si, Ge, P, and Sn, it is been well understood that semiconducting smart materials will rule the electronic device.
Figure shows spin polarization (Red colored in Fig. b) at the surface of GaP six-layered slab (a) and their atomic layer relaxation with respect to bulk inter layer distances.
Vacancy studies of compound semiconductors based on II-VI elements and their electronic structures.
Solid State Interfaces:
Solid State Interfaces:
Particularly, interfaces of solid state electrode and electrolytes for battery applications are focused. Atomic interactions, electronic structure, interface stability, Li-ion migrations through interface are adequate to design the new materials for solid state micro battery. Density functional theory as well as large scale modelling probe to predict such properties from new materials.
Catalysis and Carbon Materials:
Catalysis and Carbon Materials:
Designing the catalyst for hydrogen and oxygen production and exploring the reaction mechanism and reaction intermediates in water splitting are the key subjects in this branch. (Nature Energy, 2018)
Surface and Interface Magnetism:
Surface and Interface Magnetism:
Improving magnetic energy density of hard magnets is the backbone for the data storage and clean energy production. Therefore, surface of hard and soft magnets, magnetic multilayers, and concept of exchange spring magnets are the main focus in this branch.
https://doi.org/10.1016/j.jmmm.2016.03.062
https://doi.org/10.1016/j.apsusc.2017.02.038
http://dx.doi.org/10.1063/1.4916374
http://onlinelibrary.wiley.com/doi/10.1002/pssb.201248566/full
Semiconducting and optical materials:
Semiconducting and optical materials:
Ferromagnetism in semiconducting materials is challenging at room temperature. Producing such materials is one of major objective for spintronics applications. Also the optical properties of such materials give the attractive importance to characterize them.
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