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Thoughts on nanomaterials modeling

What is nanomaterials modeling?

Materials at the nanoscale level (with size less than 100nm) have physical and chemical properties which are distinctive and different when compared to the bulk materials. One an example of such a  difference is the increase of surface area with respect to the volume at nanoscale levels, which can cause increased reactivity. Numerous investigations and research into nano-sized structures contributed to the development of new nanomaterials with significant technological and commercial impact in domains like fuel cell, semiconductor, photovoltaic, energy storage, composite related industry.[1] On the other hand, the incredible surge in increased computational power collaborated with a more efficient and decreased use of electricity according to Koomey’s law,[2] inadvertently contributed to the development and advancement of computational modeling and simulation techniques which nowadays can be performed on an ordinary laptop. Molecular modeling has been present in the academic and industrial research environment for almost half a century. However, in recent years this increase in the computational power and capacity of high performance supercomputers made the modeling of systems containing thousands of atoms more affordable than ever. Simultaneously it induced more theoretical research in an attempt to predict properties of new nanomaterials and related phenomena by means of quantum chemistry methods based on ab initio and density functional theory (DFT). nanomaterialsThree main pathways are currently unfolding when one thinks at nanomaterials modeling:

1. A fundamental research based on a thorough computational study with the clear focus to decipher the structure and mechanism of different materials at atomistic scale using various ab initio, molecular mechanics, and molecular dynamics methods. This is imperative because the knowledge gained upon the use of quantum theory could act as a connecting bridge between engineering and synthesis of novel materials with tailored properties (optical, electronic, piezoelectric, catalytic). This research is mainly performed in a university setting.

 2. Another approach which could enhance the discovery of novel nanomaterials is the so-called Integrated Computational Materials Engineering.[4] ICME  validates a multi-scale modeling method using advanced computational and cheminformatics tools which can have a direct impact on the future of computational material design, but it goes one step further to actually design  the devices based on the new nanomaterials. This way the synergy between modeling, simulation, and experiment can not only contribute to the development of novel, better materials and devices, but also reduce development costs and speed up the time needed to market the final products.[3]

3. A third path where modeling and simulation could have an impact is related to the environmental effect of future new nanotechnology related products, more specifically the toxicity of nanomaterials.[5] Methods based on quantitative structure-relationship (QSAR) methods are well established and heavily used in drug design.  Predicting the risk and effect on the human health and environment using nano-QSAR methods is a challenge, [6] but will certainly gain support and lead to the development of new descriptors and methods which will aid in assessing/predicting the impact on environment of novel emerging nanomaterials.

 

Now, the question is how the materials/chemical industry relates to this? Would emerging nanotechnology start-up companies be interested to access and rely on data from modeling and simulation?

Method development is way ahead and continuously growing. Would applications of these methods open the door for novel, disruptive material design in an industrial setup, or will remain the topic of research at academic level through the various computational groups?

 

1. Mercier, J. P.; Zambelli, G.; Kurz, W. “Introduction to Materials Science” Elsevier, 2002.

2. Koomey, J.G.; Berard, S.; Sanchez, M.; Wong, H. “Implications of Historical Trends in the Electrical Efficiency of Computing” Annals of the History of Computing, IEEE, 33, 46-54, 2011.

3. Mize S. “Toward Nanomaterials by Design: A Rational Approach for Reaping Benefits in the Short and Long Term” available at: http://www.scottmize.com/images/nano_wpaper_mize.pdf (Retrieved in April, 2014)

4. Panchal, J. H.; Kalidindi, S. R., McDowell, D. L. “Key computational modeling issues in Integrated Computational Materials Engineering” Computer-Aided Design, 45, 4-25, 2013.

5. Nel, A.; Xia, T, Madler, L. et al. “Toxic potential of materials at the nanolevel” Science, 311, 622-627, 2006.

6. Gajewicz, A.; Rasulev, B.; Dinadayalane, T. C.; Urbaszek, P.; Puzyn, T.; Leszczynska, D.; Leszczynski, J. “Advancing risk assessment of engineered nanomaterials: Application of computational approaches” Advanced Drug Delivery Reviews, 64, 1663-1693, 2012.

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