Spring 2019 – Machine-Learning Prediction of Chemical Properties for Process Safety and Soft Materials

There is growing interest in applying machine learning techniques in the research of materials science. These machine learning models have enabled rapid predictions based purely on past data rather than by direct experimentation which takes a lot of time and efforts. Compared with computations/simulations, the machine learning methods don’t require that fundamental equations are explicitly solved. In this project, we will take advantage of machine learning methods to predict the chemical properties based on existing experimental data. The undergraduate researchers will learn how data acquisition and prediction were used in the real engineering world. By end of the project, one or two co-authored publications would be expected.