Computational modeling has become an important tool for scientists to both predict the properties of materials and systems and to gain a better understanding of the underlying mechanisms. This chapter is a brief yet holistic introduction to computational modeling, focusing on density functional theoretical (DFT) methods. The different types of computational modeling methods, including molecular mechanics, semiempirical, and ab initio methods, as well as the different software available for computational calculations are discussed. A step-by-step guide is presented using Gaussian16 software to introduce the basics of computational modeling based on our work with biomimetic polymer beads. However, the guide presented here is not limited to this particular system; it can be applied to any computational modeling case. The computational modeling methods for the building of the structures are described, and the calculation parameters, such as basis sets and exchange-correlation functionals, are explained. The output data and results are presented and discussed. Two simulation features were the focus of this work: (1) the simulation of the Raman spectra and (2) the different solvation environments. While some researchers in the field believe that computational simulation should be performed before the lab experiments, in fact they should be done simultaneously. This is so that the output of the experimental data can be used as the input of the computational parameters, as a form of semiempirical modeling, in order to achieve more accurate results for predicting the behavior of future experiments and understanding the atomic forces and mechanisms.
Keywords: Barcoded polymer; Computational modeling; Density functional theory; Multivariate data analysis.