Knowledge Led Structure Prediction for Nanostructures

Nanoparticles are key to a wide range of day-to-day technological applications, including petrochemical catalysis, biomedical imaging, optoelectronics, paints, inks, coatings, and nanomedicine. To develop and optimise such applications, one needs a good understanding of the atomic structures of the involved nanoparticles that is difficult to obtain from experiment alone. Similar structural models are relevant to understanding the toxicology and environmental effects of nanoparticles and their role in astrochemistry. We will develop a linked nanoscale structure prediction code (WASP@N) and web-interfaced database aimed at generating and archiving such structural models of nanostructures. This combination will provide a fast and efficient way of predicting the atomic structure of both fundamentally new systems that have never been studied before and known systems embedded in a realistic environment; e.g. in solution, in the pores or on the surface of a material, and/or with an organic capping agent, rather than isolated in a vacuum. The latter is crucial for understanding nanoparticles in many industrial processes (e.g. liquid phase nanoparticle catalysis, inks, coatings) and, for instance, nanotoxicology, but has not been done routinely before due to additional computational cost of including the environment. We strongly believe that the combination of new algorithmic approaches to be included in WASP@N and access to all low energy structures for the particles in vacuum in the cluster structure database will make predicting the atomic structure of nanoparticles insolution, for example, much more efficient and a standard technique in the repertoire of the applied computational chemist. The cluster structure database, finally, will also be useful as a stand-alone resource for experimental and computational chemists, chemical engineers, physicists, electronic engineers and toxicologists looking for information on the structure of materials on the nanoscale.

Nanoparticles are used in a wide range of day-to-day technological applications relevant to the UK economy, including petrochemical catalysis, biomedical imaging, optoelectronics, paints, inks, coatings, and nanomedicine. To develop and optimise such applications, good structural models of nanoparticles are required.

Apart from technological applications, the same models are also crucial when trying to understand and evaluate the environmental and toxicological properties of nanoparticles. Nanoparticles, both naturally occurring and manmade, play an active role in the chemistry of the upper atmosphere and hence influence the environment both in terms of the ozone (layer) destruction and climate. Due to the increasing technological usage of nanoparticles, there is need for a good understanding of nanoparticle toxicology beyond that arising from their chemical composition.

We will provide interested academic, industrial and governmental parties with both new tools to develop structural models of nanoparticles themselves and the opportunity to find models previously developed by others in a web-database we will operate. We will provide unique functionalities, for example, for generating structural models of nanoparticles in the presence of solvent, relevant to both many industrial processes and toxicological and environmental systems. The database and the validation of the data included in it, means we will provide a trusted data source regarding nanoparticles structures and properties to the academic, industrial and governmental community.

Further impact on the UK knowledge economy from the work arises through the exposure of students of the Molecular Modelling and Materials Science Engineering Doctorate centre at UCL and advanced undergraduates at Birmingham and UCL to the underlying philosophy and the developed tools and databases.