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SLIMES Research Group
South London Innovative Materials Evaluation Squad
Computational Materials Science • London South Bank University
About Us
Welcome to the SLIMES Research Group GitHub organisation! We are a computational materials science research lab led by Dr John Buckeridge at London South Bank University. Our group focuses on advancing materials modelling and simulation using computational techniques and emerging AI approaches to enable optimised, sustainable technologies for future demands.
🎯 Our Mission
At the intersection of physics, chemistry, and computer science, we utilise state-of-the-art computational tools to gain insights across diverse areas - from defects and charge transport to functional materials design. We integrate machine learning and artificial intelligence methods to enhance efficiency and discovery in our pursuit of materials innovations.
🔬 Research Areas
- Defects in Semiconductors: Understanding charge carrier interactions with defects in crystalline systems
- Energy Materials: Materials for sustainable energy applications and high-power microelectronics
- Machine Learning for Materials: AI-driven approaches for materials discovery and property prediction
- Density Functional Theory: Advanced computational modeling and simulation
- Sustainable Technologies: Materials research for environmental applications
🌐 Website
Visit our official website: slimeslab.github.io
Our website features:
- Research topics and methodologies
- Team members and collaborators
- Publications and news updates
- Interactive galleries and resources
- Open positions and opportunities
👥 Leadership
Dr. John Buckeridge - Principal Investigator
- Senior Lecturer in Thermofluids and Turbomachinery
- School of Engineering, London South Bank University
- 📧 [email protected]
- 🌐 Personal Website
- 🎓 Google Scholar
🛠️ Technologies & Tools
Our research leverages cutting-edge computational tools and frameworks:
- Simulation Software: VASP, Quantum Espresso, VESTA
- Programming Languages: Python, JavaScript, FORTRAN
- Machine Learning: TensorFlow, PyTorch, scikit-learn
- Visualisation: VESTA, Matplotlib, custom web-based tools
- High-Performance Computing: University HPC clusters
📊 Research Impact
- 75+ Research Publications
- 5,500+ Citations
- 5+ Group Members
- Multiple International Collaborations
🤝 Collaborations
We maintain active collaborations with leading research institutions worldwide, including partnerships with:
- Materials Chemistry Consortium (MCC)
- Thomas Young Centre (TYC)
- The Royal Society of Chemistry
- QEVEC Research Network
- D-Wave Systems
🔗 Connect With Us
- 🌐 Website: slimeslab.github.io
- 📧 Email: [email protected]
- 🔬 ResearchGate: SLIMES Lab
- 💼 LinkedIn: Dr. John Buckeridge
📍 Location
London South Bank University
T703 Tower Block, Division of Mechanical Engineering
103 Borough Road
London, SE1 0AA, UK
🚀 Getting Involved
For Students
We welcome talented students at all levels:
- PhD Positions: Computational materials science projects
- Master's Projects: Materials modeling and simulation
- Bachelor's Thesis: Introduction to computational research
For Researchers
- Postdoc Opportunities: Advanced research positions available
- Visiting Scholars: Collaborative research visits welcomed
- Industry Partnerships: Technology transfer and joint projects
For Developers
- Open Source: Contributing to materials science software
- Web Development: Enhancing research visualization tools
- Machine Learning: AI applications in materials discovery
Check our Open Positions page for current opportunities.
📈 Repository Organization
Our repositories are organised into several categories:
🔬 Research Projects
- Materials modelling simulations
- Machine learning models for property prediction
- DFT calculation workflows
- Data analysis and visualisation tools
🛠️ Software Tools
- Custom analysis scripts
- Workflow automation tools
- Research infrastructure code
🎓 Research Philosophy
Our approach combines:
- Literature Review: Vigorous survey of existing knowledge
- Computational Modeling: 3D materials structure modeling
- Simulation & Analysis: Advanced computational techniques
- Machine Learning: AI-enhanced discovery and prediction
- Validation: Experimental collaboration and verification
📄 License & Usage
Unless otherwise specified, code in our repositories is available under open source licenses. Please check individual repository licenses for specific terms.
Website & Branding: © 2025 SLIMES Research Group, London South Bank University. All Rights Reserved.
💝 Acknowledgments
We gratefully acknowledge support from:
- London South Bank University
- UK Research and Innovation (UKRI)
- European Research Funding Programs
- Industry Partners and Collaborators
Built with ❤️ for advancing materials science through computational modelling and AI-driven approaches.
For more information, visit our website or contact Dr. John Buckeridge at [email protected]