Top Tweets for #MachineLearned

about 2 months ago

Greetings from the 2026 @NSF CSSI/CyberTraining/SCIPE PI Meeting in Rockville, Maryland! 👋 Francesco presented our latest work on #MBX, the open-source software behind our #datadriven #manybody simulations. By combining #machinelearned low-order n-body terms trained on CCSD(T) data with #physics-based representations of many-body effects, our #datadriven #manybody potentials transfer chemical accuracy across phases. 🏄‍♀️ Interested in running your own #datadriven #manybody simulations? Check out #MBX: ✅ https://t.co/bVIx3HIxcB ✅ https://t.co/eY2zrSwbKd ✅ https://t.co/EFiOa7rtCh Big thanks to @NSF for supporting our research on #MBX and #datadriven #manybody simulations, and to the organizers, program officers, and all the presenters for a great meeting! 🙌 @UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

PaesaniLab's tweet photo. Greetings from the 2026 @NSF CSSI/CyberTraining/SCIPE PI Meeting in Rockville, Maryland! 👋

Francesco presented our latest work on #MBX, the open-source software behind our #datadriven #manybody simulations.

By combining #machinelearned low-order n-body terms trained on CCSD(T) data with #physics-based representations of many-body effects, our #datadriven #manybody potentials transfer chemical accuracy across phases. 🏄‍♀️

Interested in running your own #datadriven #manybody simulations? Check out #MBX:
✅ https://t.co/bVIx3HIxcB
✅ https://t.co/eY2zrSwbKd
✅ https://t.co/EFiOa7rtCh

Big thanks to @NSF for supporting our research on #MBX and #datadriven #manybody simulations, and to the organizers, program officers, and all the presenters for a great meeting! 🙌

@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD

324

Physical Review B @PhysRevB

about 2 months ago

#PRBTopDownload: #MachineLearned potential for fission gas diffusion in #UraniumOxide #NuclearFuels A. R. Miles, B. Monserrat, and S. C. Finkeldei Phys. Rev. B 114, 175108 – Published 4 September, 2026 ➡️ https://t.co/lB637jHkfG #OpenAccess #condmat #physics @APSPhysics

PhysRevB's tweet photo. #PRBTopDownload: #MachineLearned potential for fission gas diffusion in #UraniumOxide #NuclearFuels

A. R. Miles, B. Monserrat, and S. C. Finkeldei
Phys. Rev. B 114, 175108 – Published 4 September, 2026

➡️ https://t.co/lB637jHkfG
#OpenAccess #condmat #physics @APSPhysics https://t.co/fVzOkpAdQf

863

Physical Review B @PhysRevB

8 months ago

PRB Editors' Suggestion: #Electrostatic interactions in #atomistic and #MachineLearned potentials for polar materials Lorenzo Monacelli and Nicola Marzari Phys. Rev. B 113, 094101 ➡️ https://t.co/9qC72BUnMa #EdSugg @APSPhysics #condmat #physics

PhysRevB's tweet photo. PRB Editors' Suggestion: #Electrostatic interactions in #atomistic and #MachineLearned potentials for polar materials

Lorenzo Monacelli and Nicola Marzari
Phys. Rev. B 113, 094101

➡️ https://t.co/9qC72BUnMa
#EdSugg @APSPhysics #condmat #physics https://t.co/Qi9wGTwYZv

498

reggie @hydraete_

9 months ago

i trained a linear regression model for the first time all by myself and did all of the data setup (no really, hold ur applause, please, they don’t call me a Really Good Computer Guy for nothing) #MachineLearned

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