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Copy file name to clipboardExpand all lines: _pages/Codes.md
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We lead and contribute to the development of a range of computational packages to support our research, primarily based in Python.
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These computational tools are open-source and are used by thousands of other researchers worldwide.
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Code development is not a requirement in our group, but it can be a great way to build and showcase highly-desired skills, while greatly accelerating our research and benefitting the global research community.
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We aim to produce code that is simultaneously powerful and user-friendly, to maximise impact.
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We aim to produce code that is simultaneously powerful and user-friendly, to maximise impact.
`doped` is a comprehensive Python suite for the generation, pre-/post-processing and analysis of defect calculations, implementing the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner.
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Click the image to check out the docs site!
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`doped` is a comprehensive Python suite for the generation and analysis of defect calculations, implementing the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner.
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Click any of the images here to check out the docs sites!
`ShakeNBreak` is a defect structure-searching method (i.e. global optimisation strategy) employing chemically-guided bond distortions to locate ground-state and metastable structures of defects in solid materials. Click the image to check out the docs site!
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`ShakeNBreak` is a defect structure-searching method (i.e. global optimisation strategy) employing chemically-guided bond distortions to locate ground-state and metastable structures of defects in solid materials.
`NequIP` is an open-source code for building equivariant graph neural network (GNN) interatomic potentials (i.e. machine-learned force fields; MLFFs).
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<!-- link matbench discovery perhaps -->
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It is a highly flexible and extensible architecture, which has inspired several other community codes in this area.
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`NequIP` is an open-source code for building equivariant graph neural network (GNN) interatomic potentials (i.e. machine-learned force fields; MLFFs), which has inspired several other community codes in this area. See the [matbench-discovery leaderboard](https://matbench-discovery.materialsproject.org/) for inorganic material foundation models.
`Allegro` is an extension to `NequIP`, which implements a strictly-local GNN architecture without the use of message-passing to facilitate highly-scalable simulations (on up to a trillion atoms!).
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`Allegro` is an extension to `NequIP`, which implements a strictly-local GNN architecture to facilitate simulations of millions of atoms 🚀
`easyunfold` implements electronic band structure unfolding, which is a technique for obtaining the effective band structure of symmetry-broken systems, such as disordered/defective/alloyed/thermally-active materials.
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`easyunfold` implements electronic band structure unfolding, which is a technique for obtaining the effective band structure of symmetry-broken systems, such as disordered / defective / alloyed / thermally-active materials.
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[Cs₂(Sn/Ti)Br₆ Vacancy-Ordered Perovskite Alloys](https://doi.org/10.1021/acs.jpcc.3c05204) | Oxygen Vacancy (*V*ₒ⁰) in MgO
Copy file name to clipboardExpand all lines: _pages/Join.md
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---
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# Join Us
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We are actively recruiting motivated students and post-doctoral researchers.
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Experience with computational methods (Python / coding, machine learning, DFT...) is certainly a plus, but not required – just a willingness to learn and a passion for research!
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(Seán had no experience before his PhD and he somehow became a professor).
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Some knowledge in the physical sciences (chemistry, physics, materials science, maths) is also beneficial, but again not a dealbreaker and we are happy to train you in the necessary background.
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We have built a comprehensive internal wiki and accompanying set of group resources (code & video tutorials, condensed notes and walkthroughs) to help you get up to speed quickly, while Seán and other group members are committed to mentoring you through your growth as an independent researcher.
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We are a friendly and inclusive group, and we welcome students from all backgrounds.
We are actively recruiting motivated students and post-doctoral researchers! <br>
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You can reach out to Seán via email (sk2045[at]cam.ac.uk), and it is helpful to include a brief description of your background, a CV and any relevant experience, to help determine potential research projects.<br><br>
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Experience with computational methods (Python / coding, machine learning, DFT...) is certainly a plus, but not required, just a willingness to learn and a passion for research! (Seán had no computational experience before his PhD!).
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Some knowledge in the physical sciences – chemistry, physics, materials science, maths – is also beneficial, but again not a dealbreaker and we are happy to train you in the necessary background.
Our [Research](/research.html) page gives a flavour of what we do, but we are always open to new ideas and approaches, so if you have a specific project in mind that you would like to work on, feel free to reach out.
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## Our Commitment to You
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We have built a comprehensive internal wiki and accompanying set of group resources (code & video tutorials, condensed notes and walkthroughs) to help you get up to speed quickly. Seán and group members are committed to mentoring you through your growth as an independent researcher.
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We are a friendly and inclusive group, and we welcome all backgrounds.
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<!-- We have a weekly group meeting where we discuss ongoing projects, share ideas, and provide feedback. -->
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<!-- This collaborative environment ensures that you are never alone in your research and that help is always available when you need it. -->
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<!-- Should add a group photo here once we have one! -->
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<imgsrc="https://substackcdn.com/image/fetch/$s_!ebfk!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ffc878b00-a7af-436f-af87-af8ccb6ecd92_1024x768.jpeg"alt="One of Us"width="500px"class="img-fluid">
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As mentioned on our [Research](/research.html) page, we work on a variety of topics in computational materials science, with a focus on energy materials and machine learning. We are particularly interested in projects that involve the development of new computational methods or the application of existing methods to solve challenging problems in materials science.
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Gives a flavour of what we do, but we are always open to new ideas and approaches, so if you have a specific project in mind that you would like to work on, please feel free to reach out.
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You can reach out to Seán via email (sk2045[at]cam.ac.uk), and it is helpful to include a brief description of your background, a CV and any relevant experience, to help determine potential research projects.
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Undergraduate, Masters' and PhD students that Seán has supervised have won research awards (Catlow prize for best PhD in Chemistry, Sharp and Nyholm prizes for best MSc/MSci in Theoretical and Inorganic Chemistry respectively, ...), published papers in leading journals (_Energy & Environmental Science_, _npj Computational Materials_, _Joule_, _Matter_...) and achieved top grades for their projects.
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**Should have a photo of me somewhere!**
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<!-- _Nature Reviews Materials_ -->
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## Fellowships
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We are extremely supportive of students applying for external fellowships to fund their research, and we have a strong track record of success in this area. If you are interested in applying for a fellowship, please reach out to Seán to discuss potential projects and application strategies.
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## Post-Doctoral Researchers
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We are extremely supportive of students and postdocs applying for external fellowships to fund their research, and we have a strong track record of success in this area. There are a number of opportunities for fellowships at Cambridge.
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If you are interested in applying for a fellowship, please reach out to Seán to discuss potential projects and application strategies.
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Many of the undergraduate students I have mentored have won research awards (thesis prize, NSF GRFP, MIT poster award) and have co-authored papers in top venues (e.g. ICML 2021, RECOMB/Cell Systems 2022, RECOMB 2024/Nature Methods 2025).
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<!-- From Uthsav's website; could be useful if we do get inundated: -->
At the SAM lab, we aim to employ the most powerful computational techniques available to tackle crucial materials challenges for a better world.
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At the SAM lab, we develop and employ the most powerful computational techniques available to tackle crucial materials challenges for a better world.
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Currently, this includes quantum-mechanical (e.g. DFT, GW) and machine-learning approaches, which we use to investigate, design and develop advanced materials across a range of technologies.
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We have a particularly strong interest in materials for energy conversion (such as solar cells, photocatalysts and thermoelectrics) and energy storage (primarily batteries).
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Some core areas of focus are given below, but we are flexible to the skillsets and interests of those who join our team!
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**Collaboration and conferences (with a photo from one perhaps?)**
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We have a particularly strong interest in materials for energy conversion (such as solar cells, photocatalysts and thermoelectrics) and energy storage (e.g. batteries).
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Some core focus areas are given below, but we are flexible to the skillsets and interests of those who join our team!
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-[ShakeNBreak: Navigating the defect configurational landscape](https://joss.theoj.org/papers/10.21105/joss.04817)_Journal of Open Source Software_ 2022
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-[doped: Python toolkit for robust and repeatable charged defect supercell calculations](https://joss.theoj.org/papers/10.21105/joss.06433)_Journal of Open Source Software_ 2024
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-[easyunfold: A Python package for unfolding electronic band structures](https://joss.theoj.org/papers/10.21105/joss.05974)_Journal of Open Source Software_ 2024
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-[easyunfold: A Python package for unfolding electronic band structures](https://joss.theoj.org/papers/10.21105/joss.05974)_Journal of Open Source Software_ 2024
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<br><br>
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Some of these studies are showcased on Seán's [YouTube](https://www.youtube.com/c/Se%C3%A1nRKavanagh) channel!
Copy file name to clipboardExpand all lines: _pages/index.md
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We work at the intersection of materials science, chemistry, physics and artificial intelligence (AI); developing and deploying techniques from quantum chemistry (e.g. DFT), solid-state physics and machine learning (ML) to understand the atomic-level properties of materials.
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Using our insights from these calculations, run on massive high-performance computers, we predict, design and optimise the performance of materials in real-world technologies.
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The team is led by Dr. Seán Kavanagh, and is part of the vibrant materials modelling community at Cambridge, including the [Lennard Jones Centre](https://ljc.group.cam.ac.uk/).
The team is led by Dr. Seán Kavanagh, and is part of the vibrant materials modelling community at Cambridge, including the [Lennard Jones Centre](https://ljc.group.cam.ac.uk/).
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## Interested in joining the SAM Lab?
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We are actively looking for motivated students and postdocs!
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Check out our current [Research](Research) interest areas, and the [Join](Join) page if you are interested in being part of the team.
Travelling to conferences to present and discuss ideas with other researchers, with time to be a tourist and explore, is one of the best parts of the job. From Psi-K 2025:
- Want to link codes, to get a flavour of what we do (In research?). Could maybe have a separate Codes page like Alex? Stuff from doped/SnB docs sites could be nice, like SnB gif etc
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- Likewise, YouTube
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- Re-run through and make more succinct
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- Update links on SAM Lab github and description, add link to website at top of README.
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- Link on kavanase website etc
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- Update our socials descriptions
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- Update commonninja image URL
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### Ideal TODOs:
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- Style etc; after content. I'd like a different font, like Alex's site.
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- Eye-catching image -- something that makes it look cool but accessible?
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- Eye-catching image -- something that makes it look cool but accessible? Happy with what we have?
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