Zobrazeno 1 - 10
of 95
pro vyhledávání: '"N M Anoop, Krishnan"'
Publikováno v:
Frontiers in Materials, Vol 11 (2024)
Artificial intelligence (AI) and machine learning (ML) have enabled property-targeted design of glasses. Several machine learning models and open-source tools in the literature allow researchers to predict the optical, physical, mechanical, and elect
Externí odkaz:
https://doaj.org/article/fc6cfa4dcddc43aa8da52602259e210b
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035049 (2024)
The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derived as functionals based on observables such as positions and velocities
Externí odkaz:
https://doaj.org/article/df2f09003603451a9802e210a79c593a
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-11 (2022)
Abstract A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments in natural language processing, such as Bidirectional Encoder Representations from Tran
Externí odkaz:
https://doaj.org/article/e174f1f4299849d49c8790fb5d7c64dd
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
Abstract Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To ad
Externí odkaz:
https://doaj.org/article/fd3045a55ad34dad90972c487c16a063
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015003 (2023)
Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional approaches require the knowledge of several abstract quantities such as the energy or force to
Externí odkaz:
https://doaj.org/article/6bbca1e256934faebdb91543242408fd
Publikováno v:
JPhys Materials, Vol 6, Iss 2, p 024003 (2023)
Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relati
Externí odkaz:
https://doaj.org/article/5d48e69d08644f0ca4562709eafea276
Autor:
Vineeth Venugopal, Sourav Sahoo, Mohd Zaki, Manish Agarwal, Nitya Nand Gosvami, N. M. Anoop Krishnan
Publikováno v:
Patterns, Vol 2, Iss 7, Pp 100290- (2021)
Summary: Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision
Externí odkaz:
https://doaj.org/article/c5e94239fb4146d7a332d3124ec1874f
Autor:
Amreen Jan, N. M. Anoop Krishnan
Publikováno v:
The Journal of Physical Chemistry C. 126:15037-15045
Autor:
Jared Rivera, Jonathan Berjikian, R. Ravinder, Hariprasad Kodamana, Sumanta Das, Naresh Bhatnagar, Mathieu Bauchy, N. M. Anoop Krishnan
Publikováno v:
Frontiers in Materials, Vol 6 (2019)
Most glasses are often exposed to impact loading during their service life, which may lead to the failure of the structure. While in situ experimental studies on impact-induced damage are challenging due to the short timescales involved, continuum-ba
Externí odkaz:
https://doaj.org/article/9ebb829bf521429180f5ba367c2a295e
Publikováno v:
ACS Applied Nano Materials. 5:4812-4822