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Seán Kavanagh

@kavanaghsean.bsky.social
169 followers 133 following 81 posts

sam-lab.net Computational chemist, physicist, material scientist? Who knows... Asst Prof in Simulation of Energy Materials at the University of Cambridge (Chemistry) Formerly Environmental Fellow @harvard.edu

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Seán Kavanagh @kavanaghsean.bsky.social · 27/05/2026
We discuss burgeoning efforts to use data-driven and machine learning methods for defect simulations; in our contribution to the 100 Years of Point Defects special collection in MRS Bulletin: Plenty of challenges and opportunities alike! link.springer.com/article/10.1...
link.springer.com
Accelerating point-defect simulations using data-driven and machine learning approaches - MRS Bulletin
Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trai...
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Seán Kavanagh @kavanaghsean.bsky.social · 28/01/2026
Honoured to be named a Wiley Research Hero (Open Science Advocacy)! 👨‍💻 Open access data, software, papers etc are transforming research, esp computational materials science. Increasing recognition for community contributions will motivate young researchers to follow suit!👨‍💻📈
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Seán Kavanagh @kavanaghsean.bsky.social · 26/01/2026
Free-to-read link here! rdcu.be/eZJ86
rdcu.be
Guidelines for robust and reproducible point defect simulations in crystals
Nature Reviews Materials - Point defects critically influence material properties and require accurate computational modelling for reliable predictions. This Perspective outlines best practices for...
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Seán Kavanagh @kavanaghsean.bsky.social · 21/01/2026
@agsquires.bsky.social @aronwalsh.github.io @scanlond81.bsky.social
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Seán Kavanagh @kavanaghsean.bsky.social · 21/01/2026
Defect simulations are notoriously sensitive to the many choices required 👨‍💻📊 In this Perspective, we highlight best practices in calculating 𝘢𝘯𝘥 𝘳𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 defect properties, hoping to establish guidelines for robust, transparent and reproducible defect simulations 🌟 www.nature.com/articles/s41...
nature.com
Guidelines for robust and reproducible point defect simulations in crystals - Nature Reviews Materials
Point defects critically influence material properties and require accurate computational modelling for reliable predictions. This Perspective outlines best practices for defect simulations using supe...
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Seán Kavanagh @kavanaghsean.bsky.social · 13/10/2025
www.linkedin.com/posts/aps-di...
linkedin.com
Nicholas Metropolis Award | APS Division of Computational Physics
Big congratulations to Seán Kavanagh - the 2025 recipient of the Nicholas Metropolis Award for Outstanding Doctoral Thesis Work in Computational Physics: For developing computational techniques and op...
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Seán Kavanagh @kavanaghsean.bsky.social · 13/10/2025
Applications are closed for this year, but any outstanding graduating PhD students in computational physics should next year!
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Seán Kavanagh @kavanaghsean.bsky.social · 13/10/2025
Just spotted this editorial on the @apsphysics.bsky.social DCOMP Metropolis and Rahman awards! I was happy to share some thoughts with André Schleife & Koblar Alan Jackson who put this editorial-interview together, along with Chris Van de Walle. journals.aps.org/pre/abstract...
journals.aps.org
Editorial: DCOMP's 2025 Rahman and Metropolis Awards
Phys. Rev. E 112, 030001 (2025)
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Seán Kavanagh @kavanaghsean.bsky.social · 30/09/2025
Nice!
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Seán Kavanagh @kavanaghsean.bsky.social · 30/09/2025
Yes! Doped and ShakeNBreak manage other parts of the defect workflow, such as defect enumeration, symmetry, thermodynamics etc, along with input file generation and calc parsing - e.g. doped has been used with AiiDA, atomate2, quacc etc, so they are complimentary to its functionality!
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Seán Kavanagh @kavanaghsean.bsky.social · 30/09/2025
We briefly mention the development of workflow tools and high throughput studies as one of the motivating factors for better reproducibility and established guidelines, but don't go into more detail on their use as we're not the experts there!
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Seán Kavanagh @kavanaghsean.bsky.social · 30/09/2025
Thanks Janine! Yes absolutely, workflow tools should definitely be able to help for reproducibility and throughput here. I think defects are a challenge to workflow tools given the many steps and complexities, but definitely still doable
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Seán Kavanagh @kavanaghsean.bsky.social · 29/09/2025
Led by @agsquires.bsky.social with myself, @aronwalsh.github.io & @scanlond81.bsky.social
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Seán Kavanagh @kavanaghsean.bsky.social · 29/09/2025
Defect calculations have many pitfalls and key considerations for achieving good accuracy 🎯 In this perspective, we discuss these issues, how to avoid and how we can make defect simulations more reproducible – particularly important with more ML developments! 📊 chemrxiv.org/engage/chemr...
chemrxiv.org
Guidelines for robust and reproducible point defect simulations in crystals
Many physical properties of functional materials are governed by their impurities rather than their bulk characteristics. Defects in crystals can activate electronic and ionic conductivity, create act...
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Reposted by Seán Kavanagh
Alex Squires @agsquires.bsky.social · 13/03/2025
Very gracious for David to let me off the leash on this one. Kick-started an agyrodite obsession (though I may be a bit late to the party on this one)
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Seán Kavanagh @kavanaghsean.bsky.social · 15/09/2025
The Dept of Chemistry at the University of Cambridge is hiring for an Assistant Professor in Theoretical Chemistry! www.ch.cam.ac.uk/job/52637
ch.cam.ac.uk
University Assistant Professor | Yusuf Hamied Department of Chemistry
Applications are invited for a University Assistant Professor to work in the area of theoretical chemistry, broadly defined, to be taken up in October 2026 (or earlier, by agreement). The successful a...
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Reposted by Seán Kavanagh
IOP Publishing @ioppublishing.bsky.social · 01/09/2025
JPhys Energy proudly presents the 2025 Emerging Leaders Collection, a showcase of groundbreaking research from early-career scientists shaping the future of energy. Explore the collection and meet this year’s winners: ow.ly/74fs50WP55y
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Seán Kavanagh @kavanaghsean.bsky.social · 13/09/2025
Thanks Andrew!!
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Seán Kavanagh @kavanaghsean.bsky.social · 13/09/2025
Thanks very much David! Tried to run into you at Psi-k to say hello but didn't get to!
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Seán Kavanagh @kavanaghsean.bsky.social · 11/09/2025
Certainly not news to anyone who knows me 😅 But please share with prospective students! 🙌
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Seán Kavanagh @kavanaghsean.bsky.social · 11/09/2025
I am incredibly grateful for the support of my mentors, collaborators, friends and colleagues over the past few years – too many to tag, beyond the main ones: @scanlond81.bsky.social @aronwalsh.github.io @boriskozinsky 🙌
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Seán Kavanagh @kavanaghsean.bsky.social · 11/09/2025
sam-lab.net (Please share!) Our lab – the Simulation of Advanced Materials (SAM) Lab – will use state-of-the-art computational methods to design and develop next-generation materials; primarily targeting energy applications ⚡️
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Seán Kavanagh @kavanaghsean.bsky.social · 11/09/2025
I will be joining the University of Cambridge as an Assistant Professor in the Yusuf Hamied Department of Chemistry! 🧪🎉 𝐈 𝐚𝐦 𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐫𝐞𝐜𝐫𝐮𝐢𝐭𝐢𝐧𝐠 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬, and am very keen to support fellowship applications – visit our website for details! ⬇️
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Seán Kavanagh @kavanaghsean.bsky.social · 08/09/2025
...more recently 𝗡𝗲𝗾𝘂𝗜𝗣 & 𝗔𝗹𝗹𝗲𝗴𝗿𝗼 (nequip.readthedocs.io), using foundation models we have been training with the accelerated infrastructure, now on Matbench Discovery: matbench-discovery.materialsproject.org
lnkd.in
LinkedIn
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Seán Kavanagh @kavanaghsean.bsky.social · 08/09/2025
This work made heavy use of 𝗱𝗼𝗽𝗲𝗱 (defect simulation package – lnkd.in/eU4pggmg), 𝗠𝗔𝗖𝗘 (MLIP – lnkd.in/eHBXxhxV), some 𝗦𝗵𝗮𝗸𝗲𝗡𝗕𝗿𝗲𝗮𝗸 (defect structure-searching – lnkd.in/earFF_sX) and...
lnkd.in
LinkedIn
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Seán Kavanagh @kavanaghsean.bsky.social · 08/09/2025
Thank you for the feature IOP Publishing! I'm honoured to be included in the Emerging Leaders collection. Article Link: (identifying split vacancy defects with electrostatics, DFT & MLIPs): lnkd.in/eEdrB2pk
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
Have a read if you're interested!
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
I think this shows exciting potential for MLFFs in defect modelling, but with caveats... they fail dramatically for non-fully-ionised charge states where localisation matters! They work here due to the enormous configuration space but with relatively simple underlying energetics
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
This allows an efficient tiered screening; scanning 𝘢𝘭𝘭 compounds in the ICSD & Materials Project database for split cation vacancies
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
Indeed, due to the relatively simple underlying energetics (primarily electrostatics and strain), this problem is well-suited to MLIPs. I find that foundation models (MACE, NequIP, Allegro -- stayed tuned for the latter!) successfully predict split vacancy formation in most cases
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
We can't just enumerate all potential split vacancy configurations; the search space is enormous (>1000s of candidate geometries per defect). I find instead that electrostatic models can greatly reduce this space, as electrostatics dominate energetics for these 'stoichiometry-conserving' complexes
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
Unfortunately, they are very challenging to identify with current defect structure-searching methods (e.g. 𝗦𝗵𝗮𝗸𝗲𝗡𝗕𝗿𝗲𝗮𝗸) due to their 'non-local' nature, as most of these methods employ some form of 'local' structure searching techniques
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
Vacancy defects can sometimes transform to split-vacancies, with dramatic changes in energy & behaviour, e.g. in Ga₂O₃ as discovered by Joel Varley. They have only been witnessed in a handful of cases – are they inherently rare or have we just not had the tools to find them?
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Seán Kavanagh @kavanaghsean.bsky.social · 15/07/2025
Machine learning can be powerful for modelling defects, but currently only in select cases. MLIPs (& geometric/electrostatic tools in doped) allow screening for challenging 'non-local' defect reconstructions (split vacancies) in all ICSD/MP solids, w/caveats iopscience.iop.org/article/10.1...
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Seán Kavanagh @kavanaghsean.bsky.social · 02/07/2025
Collaboration with @uclchemistry.bsky.social @imperialmaterials.bsky.social @upc.edu @unibirmingham.bsky.social
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Seán Kavanagh @kavanaghsean.bsky.social · 02/07/2025
Starting with a visit to London in 2023, Cibrán began a deep dive on defects in pnictogen chalcohalides (BiChX), finding the chalcogen vacancy to dominate recombination (similar to Sb2Se3!). He shows that selective anion substitutions can mitigate their effect! pubs.acs.org/doi/10.1021/...
pubs.acs.org
Chalcogen Vacancies Rule Charge Recombination in Pnictogen Chalcohalide Solar-Cell Absorbers
Pnictogen chalcohalides (MChX) represent an emerging class of nontoxic photovoltaic absorbers, valued for their favorable synthesis conditions and optoelectronic properties. Despite their proposed def...
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Reposted by Seán Kavanagh
arXiv bot (cond-mat) @krxiv-cond-mat.bsky.social · 28/04/2025
Chalcogen Vacancies Rule Charge Recombination in Pnictogen Chalcohalide Solar-Cell Absorbers arxiv.org/pdf/2504.18089 Cibrán López, Seán R. Kavanagh, Pol Benítez, Edgardo Saucedo, Aron Walsh, David O. Scanlon, Claudio Cazorla.
arxiv.org
https://arxiv.org/abs/2504.18089
arXiv abstract link
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Seán Kavanagh @kavanaghsean.bsky.social · 18/06/2025
Available on the development branches; - Complex defect multiplicities, symmetries and degeneracies - N-dimensional chemical potential heatmap plotting using fixed values (to reduce to 3-D) - Defect "stenciling" to regenerate (relaxed) geometries in arbitrary supercells...
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Seán Kavanagh @kavanaghsean.bsky.social · 18/06/2025
- Tutorial for generating polaron distortions with ShakeNBreak: shakenbreak.readthedocs.io/en/latest/Sh... - Many efficiency updates. - Miscellaneous minor bug fixes, improvements and docs updates github.com/SMTG-Bham/Sh...
shakenbreak.readthedocs.io
ShakeNBreak for Polarons — ShakeNBreak
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Seán Kavanagh @kavanaghsean.bsky.social · 18/06/2025
- Site-competition handling in defect concentrations; see doi.org/10.26434/che... - Include 'adsorbate' interstitial sites for structures with significant vacuum volume - Improved algorithm for defect site clustering (for plotting & concentration analyses)
doi.org
Defect Tolerance via External Passivation in the Photocatalyst SrTiO3:Al
The efficiency of solar-to-energy conversion in semiconductors is limited by charge carrier recombination, often via defect-induced gap states. Although some materials exhibit an intrinsic defect tole...
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Seán Kavanagh @kavanaghsean.bsky.social · 18/06/2025
doped (3.1.0) and ShakeNBreak (3.4.2) have had new releases! - Streamlined chemical potential handling - Auto-compatibility checks w/competing phases calculation settings (as for defects) – common pitfall - Directly parse spin magnetisation (incl SOC) ... github.com/SMTG-Bham/do...
github.com
Release 3.1.0 · SMTG-Bham/doped
Update chemical potentials code: Handle recent breaking changes in pymatgen (Apr 2025). Auto check compatibility of INCAR\s and POTCAR\s in competing phases calculations (as already done for super...
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
We find a high sensitivity of the band edges to lattice parameters (i.e. deformation potentials) in t-Se, which combined with v low elastic constants (vdW-bonded) indicates significant thermal fluctuations and strain effects
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
We highly encourage sharing these outputs in defect theory papers! Quick and easy way to ensure reproducibility and queryability. Of course, this is in addition to making the relevant raw data available (which doped readily sums to lightweight & readable JSON files too)
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
doped automatically outputs dataframes/tables to aid reproducibility (and reduce manual efforts), showing all contributions to formation energies, estimated charge correction errors, symmetries, degeneracies etc
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
In particular, we find many energy-lowering reconstructions of defect geometries using ShakeNBreak, which are missed by standard/rattled relaxations (incl the V_Se^0 bipolaron mentioned above). Full details in the SI for full reproducibility 🤝
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
The theory portion was primarily performed using doped (doped.readthedocs.io/en/latest/) and ShakeNBreak (shakenbreak.readthedocs.io/en/latest/), which can expedite and expand defect analysis, with many useful tools. Some directly-output plots in the next reply ⬇️
doped.readthedocs.io
doped — doped
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
Extended defects / interfaces are often easier to engineer away than unavoidable 𝘱𝘰𝘪𝘯𝘵 defects (lacking the same entropic driving force), so this is an exciting indication of the potential for t-Se PV! 📈
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
Overall, our results suggest a strong tolerance to 𝘱𝘰𝘪𝘯𝘵 defects in t-Se, and indicate that GBs/interfaces are the key limiting factors. This aligns with recent DLTS and DLCP measurements, which suggested extended defects to be dominant in t-Se (refs & details within).
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Seán Kavanagh @kavanaghsean.bsky.social · 15/04/2025
Calculating impurity formation energies (w/doped & SnB), we find strong valence alternation -> amphoteric & charge compensation for H, pnictogens & halogens. Chalcogens are electrically neutral. F contributes the strongest to hole doping, but still relatively weak (~10¹² cm³)
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