Join Our Team! I am looking for TWO postdocs @UHouston@uh_chem!
1️⃣ Luminescent Materials: Ideal for those with a background in machine learning, materials synthesis, or spectroscopy. Focus on ML-guided discovery for lighting/display materials 💡 🖥️
We have an opening for a postdoctoral researcher in the Materials Theory group at ETH. If you're interested in figuring out how to quantify whether one material is more or less chiral than another then I'd be happy to hear from you: https://t.co/Imwhv9F2Go
It was a pleasure to make a small contribution to a huge effort to review advances in #AI4Science led by @ShuiwangJi with a focus on incorporating symmetry in physical systems.
@tesssmidt
🔬 An in-depth yet intuitive discussion on symmetry, as well as explainability, out-of-distribution generalization, large language models, and uncertainty.
📖 Access categorized lists of resources to enhance learning and education.
🌐 Website: https://t.co/3p0EYy37ch.
Nicola Spaldin @NicolaSpaldin is proof you don't need to have studied physics to do great physics research: she trained as chemist and sees herself as a material scientist and is now doing world-leading work on ferroelectrics.
https://t.co/lLZxC3xpbv
🧵(1/6) We propose EquiformerV2 with state-of-the-art results on all OC20 tasks and AdsorbML.
Joint work with @bwood_m , @abhshkdz from @OpenCatalyst and @tesssmidt .
Besides, thank @_akhaliq for tweeting before.
Paper: https://t.co/KF4bYI9lhT
Code: https://t.co/kYJDiN9shx
Exciting to see it published! Thank you for the nice collaboration 😉 and I feel happy that our prediction of such an interesting structure 🐛 got confirmed! Any other phase to be discovered? 😬https://t.co/ALAhIkkudd
Very happy that my poster about discovering new nitride perovskites was selected among the top 10%!
In case you missed it or want to have a look…
Thanks to @scanlond81@lonepair @danwdavies and @1stbz for the collaboration!
Interested in discovering low-energy polymorphs of your favourite material? We propose a method combining ML+DFT+Symmetry
In collaboration with @NicolaSpaldin and @MansouriTehrani
Physics-Guided Descriptors for Prediction of Structural Polymorphs https://t.co/frScoHebPc
Charge quadrupoles or hexadecapoles? Check out our latest paper just posted on arXiv to learn about the role of charge multipoles on the phase transition in Cs2TaCl6 @NicolaSpaldin
https://t.co/0Ki3vrIToU
@MansouriTehrani talks about treading a balance between machine learning and domain knowledge for machine learning of functional materials including a cool study of structural polymorphs of ferroelectrics! #selw22
Check out our new preprint on the machine-learning enabled first principles calculation of temperature dependent properties in quantum paraelectric KTaO3. https://t.co/IuaEOl9vRj
Bastien and Aria are trying to drag me into the 21st Century with our collaboration using Machine Learning to predict new structural phases of BiFeO3: https://t.co/bgF1BfeKEd @BastienGrosso@MansouriTehrani