🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Congratulations to Dr. Jenny Coulter for a successful defense!! 🍻🎉

Congrats to Yu for passing her defense! 🍻 Yu Xie

New Scientist article features our recent large-scale biomolecular dynamics demonstration.

🚨New preprint is available: arxiv.org/abs/2306.00901🚨 We construct a machine-learned force field (MLFF) and pair it with simulated and experimental X-ray absorption data to unravel the structural evolution of small platinum (Pt) nanoparticles (NPs) under hydrogen atmospheres.

I was just dreaming of this when designing and prototyping the AiiDA framework at Bosch Research back in 2009, and in collaboration with EPFL from 2012... It's now here, congrats to the AiiDA team! #Bosch #EPFL #MicrosoftAzure quantum.microsoft.com/en-us/our-stor…

FLARE is powering simulation AI on Microsoft Azure Quantum, with AiiDA... Hope we get new materials faster! Thanks to amazing developers at MIR: Jon Vandermause, Yu Xie, Anders Johansson, Steven Torrisi, Lixin Sun, Andrea Cepellotti, Simon Batzner, Cameron Owen, David Lim.

Please join the upcoming virtual workshop “Machine Learning Potentials - StAtus and FuturE” July 17-19 11am-3pm EDT. Learn about key advancements and opportunities in MLPs. The workshop is free and all are welcome! MLP-SAFEworkshop.eventbrite.com #AI4science

We are thrilled that Allegro was selected as one of the finalists for this year's "Nobel Prize for Supercomputing", the ACM Gordon Bell Prize. Congratulations to the team, pictured here happily finalizing the submission. arxiv.org/abs/2304.10061

Looking forward to a week of chemistry at #ACSFall2023. Check out our three invited and four contributed presentations on accelerated MD for catalysis, surface reconstructions, battery solvents, coarse graining, and ML DFT functionals.

Happy to announce that our work using Bayesian force fields (FFs) for the unbiased simulation of Au surface reconstructions is out now: arxiv.org/abs/2308.07311. We construct a FF using the FLARE code that is able to capture each of the low-index surface reconstructions of Au. 🧵

🚨Update to the TM23 paper using FLARE and NequIP is now on arXiv (arxiv.org/abs/2302.12993)🚨 We now provide an explanation for the observed error trends in force label learning by toggling parameters in the underlying quantum mechanical method and via perturbation theory. 🧵

Very honored to be elected an APS Fellow and very grateful to students and collaborators for their work and support! American Physical Society #physics buff.ly/46AmQVb

Running MD simulations with ML force fields? Consider learning the scale separation for a potential ~2-4x speed boost using Multi-scale integration: working paper: arxiv.org/abs/2310.13756 Great collaborating with Alby Musaelian, Anders Johansson, Tommi Jaakkola, and Boris Kozinsky

NequIP potentials trained at scale Google DeepMind: GNoME models discover 2.2M (380,000 stable) crystals, expanding the space of materials known to humanity (OQMD+MaterialsProject+WBM) by x10! Already 736 of these materials synthesized by LBNL and others. dpmd.ai/GNoME-AI

NequIP architecture from Materials Intelligence Research @ Harvard + compute, data & brains Google = another leap in accuracy, this time in the universe of all known materials.

🏆Nice way to start the winter break⛄️

💡Open position for Professor in Applied Mathematics at Harvard Harvard SEAS with focus on Computing and AI for Science, Engineering, and Society. Emphasis is on development of applications with strong mathematical and computing foundations. Apply by 12/31/23. academicpositions.harvard.edu/postings/13191

Discover our simple guidelines for training accurate and transferable equivariant ML interatomic potentials for ionic liquid mixtures. Test them on your systems and let us know your results! The Journal of Physical Chemistry #IonicLiquids #MachineLearning DOI: doi.org/10.1021/acs.jp…