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Ethics in Nanotechnology and Society Perception
Published in Vineet Kumar, Nandita Dasgupta, Shivendu Ranjan, Nanotoxicology, 2018
Besides the above-mentioned experimental studies there were also salient theoretical works. Studies in computational nanotechnology (Srivastava and Atluri 2002; Srivastava et al. 2003).Modelling of molecular tools for atomically-precise chemical reactions (Drexler 2004).Work in modelling the operation of molecular machine designs (Merkle 1991).Work in computational materials science for nanostructures (Merkle 1991).Theoretical tools and establishing the principles of design for a wide variety of single molecular functional nanomachines (Kolomeisky and Fisher 2007).The design and synthesis of artificial molecular motors (Sahoo et al. 2007).
Computational Studies in CNT-MMCs
Published in Andy Nieto, Arvind Agarwal, Debrupa Lahiri, Ankita Bisht, Srinivasa Rao Bakshi, Carbon Nanotubes, 2021
Andy Nieto, Arvind Agarwal, Debrupa Lahiri, Ankita Bisht, Srinivasa Rao Bakshi
The understanding of the formation of microstructure is essential in controlling the process. Modeling of microstructure is an active research area in the field of computational materials science. Phase field modeling has been developed for studying several phenomena such as microstructural evolution during solidification, grain growth and coarsening, precipitation reactions, spinodal decomposition, and other second-order transformations [22, 23]. Some open-source software like the mesoscale microstructure simulation project (MMSP) is also available [24]. It may be possible to use some of these techniques to predict the effect of the presence of CNTs on the microstructure evolution.
Smart War on COVID-19 and Global Pandemics
Published in Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya, Computational Modeling and Data Analysis in COVID-19 Research, 2021
Anil D. Pathak, Debasis Saran, Sibani Mishra, Madapathi Hitesh, Sivaiah Bathula, Kisor K. Sahu
The present chapter is enough to convince us that a huge amount of research works is being undertaken that can form the basis of our fight against future global pandemics. However, what is seriously lacking is as follows: (i) An integrated technological framework that can put the diverse computational infrastructure in a systematically integrated framework. Two analogies may be of interest here. The first analogy involves Hadoop and, in particular, the way it did try to create a software framework for distributed storage and processing of “Big data” using MapReduce, which was originally discovered by Google. There is an urgent need to develop a similar integrated framework for the model space, though it might be much tougher. The second analogy is about Integrated Computational Materials Engineering (ICME) framework, which aims to integrate different computational materials science models that are poles apart in their capabilities in handing the system size, time, and length scales. (ii) A collective will of policymakers and politicians to drive such a metamorphic technological transformation. Humanity has witnessed how nearly unthinkable scientific projects can be successfully pursued if there is a clear intent. The Human Genome Project, CERN, and ITER are some ready examples. The scientific community therefore should seriously debate how to create a truly global initiative in preparation for future pandemics. (iii) Participation of common people can do wonders. This is one critical area that should never be overlooked. No matter how sophisticated our technological architecture is, it will never be successful without decisive and active participation by all. Open research, crowdfunding, and crowdsourcing have started but their reach is still insignificant and a lot more new initiatives are necessary. The entire community should seriously introspect how to build the bridge from both ends.
Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites
Published in Mechanics of Advanced Materials and Structures, 2022
Nang Xuan Ho, Tien-Thinh Le, Minh Vuong Le
Computational tools always play an important role in the revolution of materials science [9, 10]. Indeed, from DFT to Molecular Dynamics simulations, the more efficient codes are developed, the more materials science progresses are made. Moreover, these large-scale simulations provide an enormous amount of data [11], as the typical example Materials Project mentioned above, gathering more than 120,600 inorganic compounds, 35,000 molecules as well as 530,000 nanoporous materials. In another investigation, Gómez-Bombarelli et al. [12] have combined theory and numerical methods to study the behavior of materials from a database of 1.6 × 106 organic light-emitting diode molecules. Another type of computational approach to study the behavior of composite materials is homogenization-based approaches. However, this type of methods also requires a lot of time and resources. For example, in [13], to solve a multi-scale problem involving 1.3 billion unknowns, the authors needed 5 days of computing, which is quite a lot. Consequently, in order to deal quickly and efficiently with such large datasets, a computational revolution in computational materials science has come involving Artificial Intelligence (AI) and Machine Learning (ML) methods. The concept of AI models, which is generally illustrated as a black box model, induces many challenges for engineers and researchers to fully understand and be able to apply them [14–18].
Advances in research on deformation and recrystallization for the development of high-functional steels
Published in Science and Technology of Advanced Materials, 2020
The recent development of advanced analytical techniques is remarkable, and it is now possible to clearly observe phenomena that were previously invisible. Furthermore, the progress in computational materials science has enabled us to conduct multi-scale calculations and to understand phenomena at a level of detail that used to be un-calculable and speculative. The accurate prediction of phenomena, which cannot be tested experimentally, is also becoming possible and has various implications.