Journal ArticleDOI
Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping
TLDR
The computational efficiency achieved through the proposed ML based framework without compromising quality of the analysis is demonstrated, followed by data-intensive correlation analysis, sensitivity and uncertainty quantification considering various levels of the influencing system parameters, revealing detailed computational insights on mechanical properties of graphene.About:
This article is published in Journal of Physics and Chemistry of Solids.The article was published on 2021-08-01. It has received 26 citations till now. The article focuses on the topics: Computational intelligence.read more
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Mechanical properties of graphene : effects of layer number, temperature and isotope
TL;DR: In this article, the mechanical properties of graphene, including Young's modulus, fracture stress and fracture strain, have been investigated by molecular dynamics simulations, and the simulation results show that the properties are sensitive to the temperature changes but insensitive to the layer numbers in the multilayer graphene.
Journal ArticleDOI
Machine Learning and Deep Learning in Phononic Crystals and Metamaterials A Review
Muhammad,John Kennedy,C.-W. Lim +2 more
TL;DR: In this article , the authors present a state-of-the-art literature survey in machine learning and deep learning based phononic crystals and metamaterial designs by giving historical context, discussing network architectures and working principles.
Journal ArticleDOI
Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design
TL;DR: In this paper , the uncertainties associated with internal and external parameters individually and their compound effect on the mechanical properties of graphene were systematically quantified with the aim of developing an inclusive paradigm.
Journal ArticleDOI
Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene
K. K. Gupta,Lintu Roy,Sudip Dey +2 more
TL;DR: In this paper , a polynomial chaos-Kriging-based molecular dynamics simulation framework of twisted bilayer graphene (tBLG) structures is presented to investigate the influence of stochastic parametric variations on their nano-indentation behavior.
Journal ArticleDOI
Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization
TL;DR: In this article , a probabilistic investigation of the uniaxial tensile behavior of twisted bilayer graphene (tBLG) structures is presented. But the authors do not consider the effect of the twist angle on the fracture response.
References
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Journal ArticleDOI
VMD: Visual molecular dynamics
TL;DR: VMD is a molecular graphics program designed for the display and analysis of molecular assemblies, in particular biopolymers such as proteins and nucleic acids, which can simultaneously display any number of structures using a wide variety of rendering styles and coloring methods.
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Measurement of the Elastic Properties and Intrinsic Strength of Monolayer Graphene
TL;DR: Graphene is established as the strongest material ever measured, and atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.
Journal ArticleDOI
Avogadro: an advanced semantic chemical editor, visualization, and analysis platform
Marcus D. Hanwell,Marcus D. Hanwell,Donald Ephraim Curtis,David Lonie,Tim Vandermeersch,Eva Zurek,Geoffrey R. Hutchison +6 more
TL;DR: The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology.
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Ammonia as a case study for the spontaneous ionization of a simple hydrogen-bonded compound
Taras Palasyuk,I. A. Troyan,Mikhail Eremets,Vadym Drozd,Sergey A. Medvedev,Patryk Zaleski-Ejgierd,Ewelina Magos-Palasyuk,Hongbo Wang,Stanimir Bonev,Dmytro Dudenko,Pavel G. Naumov +10 more
TL;DR: Experimental evidence is presented that the threshold pressure of ~120 GPa induces in molecular ammonia the process of autoionization to yet experimentally unknown ionic compound--ammonium amide, opening new possibilities for studying molecular interactions in hydrogen-bonded systems.
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Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
TL;DR: In this article, the root mean square error (RMSE) and the mean absolute error (MAE) are used to evaluate model performance and it is shown that the RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian.