(247aj) Qnpr (Quantitative Nanostructure Property Relationship) Study for Describing Optical Properties of Plasmon Nanomaterials Using 3D Descriptors

Datta, S., Auburn University
Herring, III, R., Auburn University
Eden, M. R., Auburn University

Computer Aided Molecular Design (CAMD) has been used to supplement and guide experimental efforts in various fields. Currently, highly predictive models can be developed as a result of improved computing hardware along with novel methodologies for model development. Decreasing dependency on experimental efforts can also reduce the environmental footprint of companies involved in product design and development.

Nanomaterials are getting significant attention due to their unique properties. Specifically, plasmon particles are the focus of much study due to their interesting optical properties. For example, these particles are being tested for various types of biological detection and cell targeting applications. In recent years, attempts have been made to develop models that relate the nanostructure aspect ratio with the optical properties of such particles. In most cases, nanodescriptors were used to develop these models.

This work aims to elucidate how the use of 3D descriptors can influence this type of model development. Genetic Algorithm (GA) in combination with Artificial Neural Network (ANN) is used extensively in the model development. Specifically, we have investigated various levels of generations of GA coupled with different numbers of hidden layers and neurons of ANN during the model identification. Both internal and external validation was used to check the predictive capabilities of the model. An analysis of the application domain was also performed to evaluate the expansive nature of the developed model.


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