Project 5: Computational Materials Science: Visualization and Machine Learning

Co-Mentors: Andre Schleife (Materials Science and Engineering) and Andrew Ferguson (Materials Science and Engineering)
Social Impact: Provide sophisticated yet intuitive and user-friendly visualization for effective materials data analysis and data dissemination to a broad scientific audience and the general public.

Project description: Modern computational materials science produces large amounts of static and time-dependent data that is rich in information. Examples include atomic geometries of complex biomolecules, condensed-matter crystals, and electron-density probability distributions. Extracting the relevant information from these data to determine the important processes and mechanisms constitutes an important scientific challenge. The availability of sophisticated yet intuitive visualization is a crucial component of effective data analysis, and is vital in disseminating results to a broad scientific audience and the general public.

In this project we use and develop physics-based ray-tracing and stereoscopic rendering techniques to visualize the structure of existing and novel materials e.g., for solar-energy harvesting, optoelectronic applications, and focused-ion beam technology. We will couple these visualization tools with Maxwell solvers and supervised machine learning algorithms to perform targeted discovery and rational design of new materials with tailored optical properties. The team will establish a powerful and intuitive platform for visualization of atomic geometries, optical reflection and transmission spectra, and time-dependent electronic excitations. This platform will allow for guided design of next-generation optical materials for use in novel lenses or energy-saving window coatings.
Working towards this goal, students will analyze and visualize atomic geometries and electron densities from first-principles simulations of excited electronic states using density functional theory (DFT) and time-dependent DFT. They will develop an open-source tool that interfaces an external Maxwell solver with the scikit-learn Python-based machine-learning library to perform supervised machine learning and guided materials design and discovery. Students will also develop codes based on the open-source ray-tracer Blender/LuxRender and the open-source yt framework to produce image files and movies from these data. Stereoscopic images will be produced that can be visualized using e.g. Google Cardboard or other virtual-reality viewers. Examples for possible outcomes can be seen here.