Seán Kavanagh

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...

www.linkedin.com/posts/aps-di...

Applications are closed for this year, but any outstanding graduating PhD students in computational physics should next year!

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...

Nice!

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!

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!

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

Led by @agsquires.bsky.social with myself, @aronwalsh.github.io & @scanlond81.bsky.social

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...

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...

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)

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

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

Thanks Andrew!!

Thanks very much David! Tried to run into you at Psi-k to say hello but didn't get to!

Certainly not news to anyone who knows me 😅
But please share with prospective students! 🙌

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 🙌

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 ⚡️

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! ⬇️

...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

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...

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

Have a read if you're interested!

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

This allows an efficient tiered screening; scanning 𝘢𝘭𝘭 compounds in the ICSD & Materials Project database for split cation vacancies

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

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

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

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?

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...

Collaboration with @uclchemistry.bsky.social @imperialmaterials.bsky.social @upc.edu @unibirmingham.bsky.social