Molecular modeling has established itself as an important component of applied research in areas such as drug discovery, catalysis, and polymers. Algorithmic improvements to these methods coupled with the increasing speed of computational hardware are making it possible to perform predictive modeling on ever larger systems. Methods are now available that are capable of modeling hundreds of thousands of atoms, and the results can have a significant impact on real-world engineering problems. The article reviews some of the modeling methods currently in use; provides illustrative examples of applications to challenges in sensors, fuel cells, and nanocomposites; and finally discusses prospects for future modeling approaches.
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APA Style
Fitzgerald, G., Goldbeck-Wood, G., Kung, P., Petersen, M., Subramanian, L. et al. (2008). Materials modeling from quantum mechanics to the mesoscale. Computer Modeling in Engineering & Sciences, 24(2&3), 169-184. https://doi.org/10.3970/cmes.2008.024.169
Vancouver Style
Fitzgerald G, Goldbeck-Wood G, Kung P, Petersen M, Subramanian L, Wescott J. Materials modeling from quantum mechanics to the mesoscale. Comput Model Eng Sci. 2008;24(2&3):169-184 https://doi.org/10.3970/cmes.2008.024.169
IEEE Style
G. Fitzgerald, G. Goldbeck-Wood, P. Kung, M. Petersen, L. Subramanian, and J. Wescott "Materials Modeling from Quantum Mechanics to The Mesoscale," Comput. Model. Eng. Sci., vol. 24, no. 2&3, pp. 169-184. 2008. https://doi.org/10.3970/cmes.2008.024.169