Prediction of Electrical Conductivity for Nanocomposites

Welcome to my Machine-Learning project to predict the electrical conductivity of Nanofillers Reinforced Polymer Nanocomposites.

The popular polymers are not normally conductive (except some conductive polymers). To make them conductive, the materials can be reinforced with conductive nanofillers. Common nanofillers are Single-wall or Multiple-wall Carbon Nanotube, Graphene. However, the electrical conductivity does not increase linearly with the amount of mixed fillers. The traditional method to predict electrical conductivity is based on an exponential equation, or to build a model of random resistor networks to calculate the conductivity.

Recently, Artificial Intelligence is growing very fast and intensively applied in many fields. This project demonstrates how to apply Tensorflow to predict Electrical Conductivity based on a known data set.

The data used in this simulation is not actual data. Instead, the data is generated with noise by using the exponential equation with different parameters for different pairs of composites. However, some resources for experimental data will be cited as references.

Let’s start with Introduction

All files on GitHub: https://github.com/linhhlp/electrical-conductivity-nanocomposite