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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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publications
Atomistic Simulation of Mixed Mode Fracture in Vitreous Silica
Published in ASME International Mechanical Engineering Congress and Exposition Proceedings, 2017
Lahkar, S., Ghosal, S. and Singh, G., 2017, November. Atomistic simulation of mixed mode fracture in vitreous silica. In ASME International Mechanical Engineering Congress and Exposition (Vol. 58448, p. V009T12A042). American Society of Mechanical Engineers.
DOI: https://doi.org/10.1115/imece2017-71321
Strength nature of two-dimensional woven nanofabrics under biaxial tension
Published in International Journal of Damage Mechanics, 2018
Recommended citation: Li, Y., Lahkar, S., Wei, Q., Qiao, P. and Ye, H., 2019. Strength nature of two-dimensional woven nanofabrics under biaxial tension. International Journal of Damage Mechanics, 28(3), pp.367-379.
DOI: https://doi.org/10.1177/1056789518769343
Multilayer Graphene-Based Thermal Rectifier with Interlayer Gradient Functionalization
Published in ACS Applied Materials & Interfaces, 2019
Recommended citation: Wei, A., Lahkar, S., Li, X., Li, S. and Ye, H., 2019. Multilayer graphene-based thermal rectifier with interlayer gradient functionalization. ACS applied materials & interfaces, 11(48), pp.45180-45188.
DOI: https://doi.org/10.1021/acsami.9b11762
Characterization and understanding of the tilt-dependence of core-loss spectra for hexagonal boron nitride
Published in Scripta Materialia, 2021
Recommended citation: Lahkar, S., Wang, X., Hemker, K. and Reddy, K.M., 2021. Characterization and understanding of the tilt-dependence of core-loss spectra for hexagonal boron nitride. Scripta Materialia, 204, p.114160.
DOI: https://doi.org/10.1016/j.scriptamat.2021.114160
Shear band formation during nanoindentation of EuB6 rare-earth hexaboride
Published in Communications Materials, 2022
Recommended citation: Karre, R., Shen, Y., Song, S., Hu, Y., Lahkar, S., Wang, X., An, Q. and Reddy, K.M., 2022. Shear band formation during nanoindentation of EuB6 rare-earth hexaboride. Communications Materials, 3(1), p.24.
DOI: https://doi.org/10.1038/s43246-022-00246-2
Intrinsic strengthening and toughening in hexagonal boron nitride by ripples
Published in Acta Materialia, 2022
Recommended citation: Lahkar, S., Jeong, B., Wang, X., Hemker, K., & Reddy, K. M., 2022, Intrinsic strengthening and toughening in hexagonal boron nitride by ripples. Acta Materialia, 229, 117845.
DOI: https://doi.org/10.1016/j.actamat.2022.117845
Atomistically calibrated Tsai-Wu criterion for orthotropic layered 2D materials and interpreting accuracy - A study on failure of hBN
Published in Mechanics of Materials, 2022
Featured on Journal Front Cover
Recommended citation: Lahkar, S., & Reddy, K. M., 2022, Atomistically calibrated Tsai-Wu criterion for orthotropic layered 2D materials and interpreting accuracy-A study on failure of hBN. Mechanics of Materials, 170, 104345.
DOI: https://doi.org/10.1016/j.mechmat.2022.104345
Competing mechanisms govern the thermal rectification behavior in semi-stochastic polycrystalline graphene with graded grain-size distribution
Published in Carbon, 2024
Recommended citation: Lahkar, S., Ranganathan, R., 2023, Competing mechanisms govern the thermal rectification behavior in semi-stochastic polycrystalline graphene with graded grain-size distribution. Carbon, 118638.
DOI: https://doi.org/10.1016/j.carbon.2023.118638
research
Graded polycrystalline graphene as Thermal Diodes
Atomistic modeling, non-equilibrium heat transfer simulations and phonon calculations reveal Competitive interplay between two thermal rectification mechanisms 
Anisotropic Failure Criteria
Anisotropic failure models were developed using new framework useful for accurate error estimation versus test data 
Distortion-induced Strengthening and Toughening
Simultaneous strengthening and toughening in hBN was discovered using atomistic modeling and TEM characterization 
Electron energy loss spectrum analysis
Relativistic scattering based quantitative EELS data analysis technique for tilt prediction in hBN 
talks
Assessment of strength criteria for multiscale modeling of multilayer two dimensional materials
Published:
teaching
Deep Learning for Mechanical, Molecular and Materials Engineering with hands-on TensorFlow in Python
Graduate course, Indian Institute of Technology - Gandhinagar, Materials Engineering, 2023
Lead Instructor for the course covering key topics in scientific machine learning focusing on neural networks.