Andy Sode Anker

🙏 Thanks to my collaborators @jla-gardner.bsky.social Louise A. M. Rosset Andrew L. Goodwin @vlderinger.bsky.social 💃 🕺
And to our funders: @novo-nordisk.bsky.social @erc.europa.eu @ukri.org 🙏

@ox.ac.uk | @oxfordchemistry.bsky.social | @somervillecollege.bsky.social | DTU | DTU Energy

Autonomous interpretation of atomistic scattering data Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories. Scattering experim...

I’m excited about the potential: helping the scattering community move toward reliable automation, and supporting autonomous labs in making real-time decisions beyond pre-trained ML models.

👉 Full preprint: arxiv.org/abs/2510.05938

Applications span molecules 🧬, crystals 💎, nanoparticles ⚪ & amorphous matter 🌫️. Our method even reveals when multiple atomic structures give identical scattering — and shows when more experimental input is needed in autonomous labs (Figure 2).

We propose a new approach: a differentiable optimisation framework that unifies scattering 📊, energetics, & chemical constraints. Instead of relying on training data, it directly refines candidate structures against experiments.

⚠️ But there’s a catch: ML models are inherently bound to their training data, making them unreliable for uncharted chemistries — exactly where discovery happens.

⏳ In my research I have built ML methods to automate this process. ML can map structures to scattering patterns and deliver split-second interpretations — enabling self-driving experiments where synthesis, measurement, & analysis are connected in a closed loop 🔁.

For over a century, X-ray ✨ and neutron ⚛️ scattering have been central to chemistry & physics. Yet interpretation remains a bottleneck — still reliant on manual expert refinement.

Autonomous interpretation of atomistic scattering data Materials with bespoke properties have long been identified by computational searches, and their experimental realisation is now coming within reach through autonomous laboratories. Scattering experim...

🚀 New preprint out! “Autonomous interpretation of atomistic scattering data” → arxiv.org/abs/2510.05938

Scattering data is still mostly analysed by hand. But what if robots could do it themselves? 🤖✨⚛️

Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$ The chalcogenide perovskite material BaZrS$_{3}$ is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's ...

BaZrS3 is an emerging solar-cell material ☀️ In a preprint led by @biancapasca.bsky.social, we describe an ML potential that can tackle the structural complexity of amorphous and polycrystalline BaZrS3. Very happy to see this online! Read more at arxiv.org/abs/2506.01517

🚀 Bringing self-driving labs to the synchrotron! 🚀

Excited to share our latest work introducing an autonomous synthesis method explicitly designed to target atomic-scale structures!

📝 Read the preprint here: lnkd.in/dmfzwEDQ

I appreciate the support from @novo-nordisk.bsky.social

A table comparing the computational power of HPC and a furnace

Pocket guide to materials discovery calculation methods (repost from the other place)

Somerville JRF Named One of 30 Best Young Climate Scientists in the World - Somerville College Oxford Dr Andy S. Anker, a Junior Research Fellow at Somerville College, has been recognised as one of the world’s 30 most promising young scientists at the Inflection Award, the world’s first-ever award ded...

🎉🎉🎉Huge Somerville congratulations to our Junior Research Fellow Dr Andy S. Anker, who has just been named one of the world’s 30 best young climate scientists at the inaugural Inflection Award. Read the full story:
www.some.ox.ac.uk/news/somervi...
#InflectionAward2025 #inflection

Thanks to @ox.ac.uk, Oxford Chemistry, @somervillecollege.bsky.social, DTU - Technical University of Denmark, DTU Energy, Novo Nordisk Foundation for making it possible 🙏

Huge thanks to the organisers and judges for this incredible opportunity and to the other awardees for inspiring me every step of the way. You are superstars! ⭐

Still taking it all in 🥹 being in Paris for the Inflection Award was a once-in-a-lifetime experience. Humbled to be named one of the 30 best young scientists working on climate solutions and to stand alongside such an incredible cohort of researchers pushing the boundaries of what’s possible.

The amorphous state as a frontier in computational materials design Nature Reviews Materials - Amorphous materials are increasingly central components of key technologies, but their structures remain challenging to study. This Perspective highlights how recent...

Can we “design” amorphous materials with useful properties? We argue that #compchem & AI are bringing us closer towards this ambitious goal: rdcu.be/d3JD6

🚨 Introducing graph-pes: a unified framework for building, training and using graph-based machine-learned models of potential energy surfaces! 🚨

#compchem #ML #ChemSky #CompChemSky

I would love to join too ☺️