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Synthetic data for atomistic #MachineLearning - our first paper in Digital Discovery! 🎉 John (John Gardner) and Zoé (Zoé Faure Beaulieu) show that a large dataset, itself generated with an ML model, can be useful for supervised & unsupervised learning tasks: pubs.rsc.org/en/content/art…

A good example of combining machine-learned potentials with chemical bonding analysis (using LOBSTER 🦞!) - both are my favorite tools to study inorganic chemistry 😆! Special thanks to Stephen and Volker Deringer for fabulous contributions!

Very excited that my Master’s project has made it into preprint form!🥳 Read all about it here: arxiv.org/abs/2305.05536 Thank you to my supervisors Volker Deringer, Andrew Goodwin and to my co-authors @thomascnicholas and John Gardner for all their hard work and support. (1/3)

A pleasure to welcome Dr Chiheb Ben Mahmoud (Chiheb Ben Mahmoud) as a new postdoctoral researcher Deringer Group Oxford! Chiheb will lead activities on #MachineLearning for spectroscopy & structural characterisation of amorphous materials, as key part of our UK Research and Innovation Frontier Research project.

New group photo! 😀 Such a pleasure to see team Deringer Group Oxford expanding and going strong - thanks to everyone's creative contributions, & to generous support by UK Research and Innovation Engineering and Physical Sciences Research Council and of course Oxford Chemistry

#MC16 by Royal Society of Chemistry is the best opportunity to network with (potential) collaborators in-person. 💡 In the photo are Joana Bustamante , Aakash Naik , Katharina Ueltzen, Janine George , Volker Deringer, Zakariya El-Machachi, Yuxing Zhou and @cer5814012 . BAM_DE Deringer Group Oxford #compchem

In case you missed it: the fabulous Daniel Thomas du Toit's "XPOT" tool for optimising hyperparameters of ML potentials was recently published over The Journal of Chemical Physics: doi.org/10.1063/5.0155…

ML acceleration for molecular dynamics in CASTEP! Hot off the press (and "Featured") at The Journal of Chemical Physics 😀 pubs.aip.org/aip/jcp/articl… #compchem #MachineLearning #OpenAccess - mini thread below (1/5)

Very pleased to present our new pre-print: "Synthetic data for pre-training neural-network potentials" 😌 See 👇🧵 for a quick summary, or have a complete read here: arxiv.org/abs/2307.15714 All thoughts and comments very welcome! 1/

What's so special about amorphous calcium carbonate (ACC)? Read about it in our Nature Chemistry paper - a collaboration with @OxColloidGroup & the Goodwin group here at Oxford Chemistry, + US colleagues. Thank you @thomascnicholas & all involved 🎉 nature.com/articles/s4155…

Device-scale atomistic modelling of phase-change memory materials (digital "ones & zeroes") – now published in Nature Electronics! 😀 We showcase #MachineLearning driven #compchem simulations that are relevant to real-world memory devices. 📄🔓 nature.com/articles/s4192… (1/5)

Delighted to share this #compchem paper from Zoé Faure Beaulieu's ongoing DPhil project – a great collaboration with @fausto_martelli at IBM Research! Zoé studied #ML models that can classify different forms of amorphous ice. Read more in The Journal of Chemical Physics doi.org/10.1063/5.0193…

Proud to share some recent work on understanding the atomic scale structure of graphene oxide and how this structure changes during thermal reduction. Really fun collabaration with colleagues here in Oxford and also an excuse to work with the amazing Miguel Caro and Tigany!

Data as the next challenge in atomistic #MachineLearning - very happy to share this Comment in Nature Computational Science 🙂 Thank you Chiheb (Chiheb Ben Mahmoud) & John (John Gardner)! Read more here: nature.com/articles/s4358…

I’m really excited to be invited to share some recent developments in the “AI in Chemistry” session. It’s great to be returning to Dublin, one of my favourite cities, to connect with so many young researchers to share ideas and results. Look forward to seeing you all there 😁🇮🇪

How random is the structure of amorphous silicon? Louise's (Louise Rosset's) new preprint addresses this question using fast ML-driven simulations & detailed analysis. Great collaboration as always with @dadrabold! Read more here: arxiv.org/abs/2407.16681

Excited to be attending my first conference as part of Deringer Group Oxford at Thomas Young Centre !

Hyperparameter optimisation for Atomic Cluster Expansion (ACE) ML potentials – the latest #compchem preprint from Deringer Group Oxford! Daniel (Daniel Thomas du Toit) and Yuxing (Yuxing Zhou) show the advantages of optimisation & importance of careful validation. Read more at arxiv.org/abs/2408.00656

The latest on XPOT, our hyperparameter optimisation tool for ML potentials! New ACE functionality, and insight into validation for automated ML fitting w/ Yuxing Zhou and Volker Deringer. Learn more or have a try: paper: arxiv.org/abs/2408.00656 xpot: github.com/dft-dutoit/xpot

Profs Volker Deringer and Fernanda Duarte have both been busy hosting a Faraday Discussions meeting in Oxford! "Data-Driven Chemistry": exploring #ML and #AI for chemical discovery. You can read about it here: chem.ox.ac.uk/article/farada… Oxford Chemistry Trinity College Faraday Discussions #FD_Data