Student projects in the CCC group

The CCC group is always eager to host motivated and hard-working undergraduate and postgraduate students from across the disciplines. We are flexible in terms of project duration (from 4-weeks internships to 2-term thesis projects) and topics, which can be tuned toward a stronger focus on either chemistry (molecular/materials design, structure-property analysis), modelling (learning various in silico simulation methods), or coding (automating simulations, training machine learning models, developing representations, structure generation, etc.). The projects we offer to students are designed to address exciting scientific questions, have clearly defined aims, fit within the project timeline, generate new fundamental knowledge, and develop new and better tools and systems. In our group, students receive daily support from other group members and get to practice their communication, presentation, and scientific writing skills.

If you are interested in pursuing a research project in our group, please get in touch with Anya.

Ideas

“Graphene Chemistry”. Graphene is a “wonder material” possessing unique electronic, optical, and mechanical properties. While it has been extensively studied by physicists and materials engineers, graphene chemistry remains under-explored and under-utilised. In this project, we will design graphene-based catalysts for such important transformations as CO2 activation and electrochemical water splitting. We will use a range of modelling techniques, including periodic density functional theory (DFT), density-functional tight-binding, high-level wavefunction theory, etc., ultimately aiming to propose promising materials for subsequent experimental testing.

“Organic Materials by Geometrical Design”. Many functional organic materials are ordered periodic systems that can be classified according to their symmetry group, tessellation class, etc. Here, we aim to answer two questions: 1. Does material’s topology define its chemical and physical properties?, and 2. Can new functional materials be designed using geometrical principles instead of (or together with) electronic structure considerations? We will use a combination of computational materials science and geometry to generate new materials design principles and test a fundamental concept of manipulating chemistry and physics through geometry.

“Chemical Machine Learning”. Machine learning has rapidly spread across all aspects of science and technology, and chemistry is no exception. In this project, we will develop new physics-based representations to enable interpretable machine learning of molecular and materials properties. We will also construct new fingerprints for machine learning the properties of large and complex molecular systems from their building blocks. These tools will not only speed up in silico predictions of chemical behaviour but will also shed the light on the structure-property patterns across vast chemical space.

"Structure Generation and Analysis". Geometric properties of porous materials are central to their uses in molecular capture, transport, and storage. In this project, we will create bespoke structure generation tools for diverse organic molecules and materials using combinatorial and fragment-based approaches. We will also include structural analysis functionalities in these tools to enable rapid quantitative assessment of voids, holes, and other interesting features. This project will involve coding in Python to create new tools and interface them with existing open-source software.

"Front-End Development". Our group is developing diverse structure generation and property prediction models for a broad range of chemistries. To deliver these tools to the broad community of chemists and materials scientists, we aim to build intuitive and user-friendly web interfaces integrating our in-house tools with on-the-fly builders and predictors. This project involves coding in Python and HTML or Java.