Search Results - "Krishnan, N. M. Anoop"

Evidence of a two-dimensional glass transition in graphene: Insights from molecular simulations by Ravinder, R., Kumar, Rajesh, Agarwal, Manish, Krishnan, N. M. Anoop

“…Liquids exhibit a sudden increase in viscosity when cooled fast enough, avoiding thermodynamically predicted route of crystallization. This phenomenon, known…”
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Enthalpy Landscape Dictates the Irradiation-Induced Disordering of Quartz by Krishnan, N. M. Anoop, Wang, Bu, Yu, Yingtian, Le Pape, Yann, Sant, Gaurav, Bauchy, Mathieu

“…Under irradiation, minerals tend to experience an accumulation of structural defects, ultimately leading to a disordered atomic network. Despite the critical…”
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Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning by Liu, Han, Zhang, Tony, Anoop Krishnan, N. M., Smedskjaer, Morten M., Ryan, Joseph V., Gin, Stéṕhane, Bauchy, Mathieu

“…Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases…”
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Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning by Lyngdoh, Gideon A., Li, Hewenxuan, Zaki, Mohd, Krishnan, N. M. Anoop, Das, Sumanta

“…Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such…”
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MatSciBERT: A materials domain language model for text mining and information extraction by Gupta, Tanishq, Zaki, Mohd, Krishnan, N. M. Anoop, Mausam

“…A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments in…”
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Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning by Yang, Kai, Xu, Xinyi, Yang, Benjamin, Cook, Brian, Ramos, Herbert, Krishnan, N. M. Anoop, Smedskjaer, Morten M., Hoover, Christian, Bauchy, Mathieu

“…The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training. However, experimental…”
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Long-term creep deformations in colloidal calcium–silicate–hydrate gels by accelerated aging simulations by Liu, Han, Dong, Shiqi, Tang, Longwen, Anoop Krishnan, N.M., Masoero, Enrico, Sant, Gaurav, Bauchy, Mathieu

“…[Display omitted] When subjected to a sustained load, jammed colloidal gels can feature some delayed viscoplastic creep deformations. However, due to the long…”
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Topological Control on the Structural Relaxation of Atomic Networks under Stress by Bauchy, Mathieu, Wang, Mengyi, Yu, Yingtian, Wang, Bu, Krishnan, N M Anoop, Masoero, Enrico, Ulm, Franz-Joseph, Pellenq, Roland

“…Upon loading, atomic networks can feature delayed irreversible relaxation. However, the effect of composition and structure on relaxation remains poorly…”
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Irradiation-induced topological transition in SiO2: Structural signature of networks' rigidity by Wang, Bu, Krishnan, N M Anoop, Yu, Yingtian, Wang, Mengyi, Le Pape, Yann, Sant, Gaurav, Bauchy, Mathieu

“…By affecting the connectivity of atomic networks, composition, temperature, or pressure can induce topological transitions between the three atomic states of…”
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Mechanics of Metal-Nanocomposites at Multiple Length Scales: Case of Al-BNNT by Anoop Krishnan, N. M, Ghosh, Debraj

“…AbstractMetal-nanocomposites are drawing attention of the composites community due to improvements in stiffness, strength, crack-bridging ability, and…”
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Atomic picture of structural relaxation in silicate glasses by Song, Weiying, Li, Xin, Wang, Bu, Anoop Krishnan, N. M., Goyal, Sushmit, Smedskjaer, Morten M., Mauro, John C., Hoover, Christian G., Bauchy, Mathieu

“…As nonequilibrium materials, glasses continually relax toward the supercooled liquid state. However, the atomic-scale origin and mechanism of glass relaxation…”
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Analytical model of the network topology and rigidity of calcium aluminosilicate glasses by Yang, Kai, Hu, Yushu, Li, Zhou, Krishnan, N. M. Anoop, Smedskjaer, Morten M., Hoover, Christian G., Mauro, John C., Sant, Gaurav, Bauchy, Mathieu

“…Topological constraint theory (TCT) has enabled the prediction of various properties of oxide glasses as a function of their composition and structure…”
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Effects of Irradiation on Albite’s Chemical Durability by Hsiao, Yi-Hsuan, La Plante, Erika Callagon, Krishnan, N. M. Anoop, Le Pape, Yann, Neithalath, Narayanan, Bauchy, Mathieu, Sant, Gaurav

“…Albite (NaAlSi3O8), a framework silicate of the plagioclase feldspar family and a common constituent of felsic rocks, is often present in the siliceous mineral…”
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Rigidity theory of glass: Determining the onset temperature of topological constraints by molecular dynamics by Hu, Yushu, Liu, Zegao, Yang, Kai, Krishnan, N M Anoop, Smedskjaer, Morten M., Sant, Gaurav, Bauchy, Mathieu

“…•We simulate by molecular dynamics a series of calcium silicate glasses.•We compute the fraction of active constraints as a function of temperature.•We…”
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Confined Water in Layered Silicates: The Origin of Anomalous Thermal Expansion Behavior in Calcium-Silicate-Hydrates by Krishnan, N. M. Anoop, Wang, Bu, Falzone, Gabriel, Le Pape, Yann, Neithalath, Narayanan, Pilon, Laurent, Bauchy, Mathieu, Sant, Gaurav

“…Water, under conditions of nanoscale confinement, exhibits anomalous dynamics, and enhanced thermal deformations, which may be further enhanced when such water…”
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Looking through glass: Knowledge discovery from materials science literature using natural language processing by Venugopal, Vineeth, Sahoo, Sourav, Zaki, Mohd, Agarwal, Manish, Gosvami, Nitya Nand, Krishnan, N. M. Anoop

“…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…”
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Building a predictive soft armor finite element model combining experiments, simulations, and machine learning by Pittie, Tanu, Kartikeya, Kartikeya, Bhatnagar, Naresh, Anoop Krishnan, NM, Senthil, Thilak, Rajan, Subramaniam D

“…Despite its relevance for law enforcement applications, the design of soft armor has mainly been based on a trial-and-error approach. In this paper, a combined…”
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Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks by Bishnoi, Suresh, Bhattoo, Ravinder, Jayadeva, Ranu, Sayan, Krishnan, N M Anoop

“…The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these…”
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Learning the dynamics of particle-based systems with Lagrangian graph neural networks by Bhattoo, Ravinder, Ranu, Sayan, Krishnan, N M Anoop

“…Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional…”
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Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships by Singla, Sayam, Mannan, Sajid, Zaki, Mohd, Krishnan, N M Anoop

“…Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal…”
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