Nanotechnology plays a crucial role in various scientific and practical applications, including healthcare, agriculture, transportation, environmental protection, energy, and aerospace.
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While nanotechnology finds applications in computer science, computers are in nanoscience research for modeling nanoparticle properties and the performance of nanodevices.1
Despite the revolutionary impact of nanotechnology on scientific advancements, concerns have arisen regarding the production, use, and environmental accumulation of nanomaterials, affecting both environmental and human health. Computational modelers can predict the exposure and hazard values associated with these nanomaterials.2
This article explores how computer modeling can simulate and significantly enhance all aspects of nanoscience research.1
The Significance of Computer Modeling in Nanoscience
Experimental studies in nanoscience face limitations due to costs, slow processes, and the difficulty of conducting and controlling experiments.3 Computational algorithms, however, facilitate the rapid analysis of nanoparticles’ behavior and properties.1
Computational modeling and simulation of nanomaterials strongly complement physical experiments. They enable the prediction of characteristics and processes under conditions difficult to replicate or achieve in a laboratory setting.4
Computer modeling in nanoscience research offers multiple advantages, such as improved interpretation of physical measurements, providing deeper insights into nanoscale phenomena.4
For instance, computational tools can process the images of nanomaterials captured by characterization techniques like scanning electron microscopy and atomic force microscopy.1 This is because optical imaging at the nanoscale is not feasible.5
Several nanomaterial databases used in computer modeling have been created recently. Machine learning and deep neural network approaches can be used to develop predictive models for various nanomaterial properties.
These models can predict the bioactivity and toxicity of novel nanomaterials before synthesis, saving costs and resources while achieving desired properties for target applications.2
Understanding Nanoscale Phenomena through Simulation
Nano- and micro-sized systems, such as biosensors, actuators, electro-mechanical systems, and probes, utilize nanostructures. A comprehensive understanding of these structures is essential to ensure proper device functioning.
The phenomena underlying the flexibility, stability, and vibrations of nanostructures —including nanosheets, nanoplates, nanobeams, nanowires, nanotubes, and nanoshells—can be analyzed using computer modeling and simulations.3
Theoretical modeling at the nanoscale can include atomistic, continuum mechanics, and hybrid atomistic-continuum mechanic approaches.
Atomistic modeling works at atomic and molecular levels, employing techniques such as density functional theory, classical molecular dynamics, and tight-binding molecular dynamics. However, this approach is time-intensive when analyzing small structures with a large number of atoms or molecules.
In such cases, continuum mechanics modeling is applied, which assumes nanostructures are homogeneous and does not consider their internal atomic compositions. This approximation, however, compromises the accuracy of crystal structure determinations.
These drawbacks can be eliminated using hybrid modeling.3
Computer Modeling Techniques
Various computer modeling techniques and applications have been developed to simulate nanostructure behaviors.
QuantumATK is an atomic-scale modeling tool written in C++ and Python. It is applicable to semiconductor physics, battery materials, polymers, catalysis, and more. It facilitates automated, complex simulations and the continuation of computations if they are not completed successfully.
Ascalaph Designer is a molecular dynamics simulation tool incorporating classical and quantum mechanical approaches. It enables the construction and editing of molecular models, geometry optimization, and dynamic simulations.1
Avogadro software offers a molecular editing and conception program for materials science, bioinformatics, and computational chemistry fields. It is generally used as a platform for chemical building, visualization, and analysis.
Nanotube Modeler is another advanced software that permits the generation of x, y, and z coordinates of nanostructures such as tubes and cones. It also aids in producing buckyballs and graphene sheets through interactive graphs.1
To enhance the accessibility of computer modeling tools for nanoscience researchers, Google introduced Espresso, an Android mobile testing framework. Espresso provides application programming interfaces that enable researchers to design user interface tests without writing code, facilitating rapid execution.1
Challenges and Limitations
Due to their intricate nature, modeling nanostructures at the atomic level presents multiple challenges. They encompass molecular, solid-state, and surface science, each with unique considerations.
The computational model must account for geometrical constructs like interfaces, edges, and surfaces, as well as the impact of strong electron-hole interactions and phenomena such as quantum confinement.
Such complex modeling requires the study of systems containing many atoms, posing a significant challenge as computational costs increase exponentially with system size.4
Despite the application of various computer modeling techniques in nanoscience research, limitations exist.
For instance, many simulation tools use classical continuum mechanics to analyze nanostructures for large-scale systems, which neglect small-scale effects like lattice spacing, electric forces, Van der Waals forces, chemical bonds, and surface effects.
However, experimental results have underscored the importance of these effects. For example, lattice spacing between atoms at the nanoscale becomes prominent, and the discrete internal structure cannot be homogenized into a continuum.3
Deep learning techniques in computer modeling necessitate well-structured nanomaterial databases. While numerous databases exist, extracting diverse structural information for predictive modeling is challenging due to access limitations and production methods.
These databases often omit necessary annotations for computer modeling, relying instead on experimental data for nanomaterial characteristics like composition, bioactivity, and physiochemical features.2
Future Directions and Emerging Trends
The convergence of novel artificial intelligence (AI) tools with nanoscience research promises to revolutionize material science.5 For example, AI applications in scanning probe microscopes can help catalog nanomaterial properties and enable easy replication of nanomaterials and their properties.
AI allows for the spontaneous characterization of complex input and output responses while modeling nano-systems.1
Efforts to enhance microscope resolution and atomic manipulation capabilities continue, yet challenges in interpreting microscope signals persist due to the dependency of tip-sample interactions on numerous parameters.
Such nanomaterial characterization issues can be solved with the use of advanced computational tools like AI.5
Computer modeling is crucial in making accurate quantitative predictions for new material design and processing.4 Computational nanotechnology will be a fundamental engineering tool for novel device designs and innovative applications.
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References and Further Reading
1. Taha, TB., Barzinjy, AA., Hussain, FHS., Nurtayeva, T. (2022). Nanotechnology and computer science: Trends and advances. Memories – Materials, Devices, Circuits and Systems. doi.org/10.1016/j.memori.2022.100011
2. Yan, X., Sedykh, A., Wang, W., Yan, B., Zhu, H. (2020). Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations. Nature Communications. doi.org/10.1038/s41467-020-16413-3
3. Chandel, VS., Wang, G., Talha, M. (2020). Advances in modelling and analysis of nano structures: a review. Nanotechnology Reviews. doi.org/10.1515/ntrev-2020-0020
4. Per, MC., Cleland, DM. (2020). Roadmap on post-DFT methods for nanoscience. Nano Futures. doi.org/10.1088/2399-1984/aba109
5. Access, O., Behgounia, F., Zohuri, B. (2020). Artificial Intelligence Integration with Nanotechnology. Journal of Nanosciences Research & Reports. doi.org/10.47363/JNSRR/2020(2)117
