Nanoporous semiconductors are used in a range of applications from sensing and gas separation, to photovoltaics, rechargeable batteries, energetic materials and micro electro mechanical systems. In most cases porosity occurs in conjunction with the competing process of amorphisation, creating a complicated material that responds differently to strain and density changes, depending on the composition. Special cases such as the archetypes can be used to guide experimentation and provide a target for synthesis. However, the transition from crystalline to amorphous is not immediate and involves a variety of changes in the chemical bonding and lattice structure. While some work has been done using archetypal analysis to study the pure types of other types of disordered systems, the archetypes of nanoporous semiconductors have never been reported. In this project you will identify archetypal crystalline or non-crystalline (defective, porous or amorphous) semiconductor materials composed of silicon, carbon and silicon carbide. The data sets will be provided.
Use archetypal analysis to find the pure crystalline and non-crystalline (defective, porous or amorphous) semiconductor materials.
Python programming and an interest in machine leaning is essential. Familiarity with platforms such as scikit-learn, Pytorch, Tensorflow and Keras is desirable.
This is a 1 semester 6pc project.
machine learning, materials informatics, archetypes