Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Shashank Pathrudkar"'
Autor:
Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal, Amartya S. Banerjee, Susanta Ghosh
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-15 (2024)
Abstract The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the co
Externí odkaz:
https://doaj.org/article/a8e2912ea0144ea3b0971bbe99c41ba7
Publikováno v:
Physical Review B. 105
We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfb486a3b35a379da4d6b73643f9fff6
https://doi.org/10.1103/physrevb.105.195141
https://doi.org/10.1103/physrevb.105.195141
Publikováno v:
Volume 12: Mechanics of Solids, Structures, and Fluids; Micro- and Nano- Systems Engineering and Packaging.
A novel machine learning model is presented in this work to obtain the complex high-dimensional deformation of Multi-Walled Carbon Nanotubes (MWCNTs) containing millions of atoms. To obtain the deformation of these high dimensional systems, existing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f15ab177181f65ded1663320606a74aa
https://doi.org/10.1115/imece2021-70463
https://doi.org/10.1115/imece2021-70463
We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4983d9870d71c46ee9418961f673ef5e
