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Materials Intelligence Research @ Harvard
187 posts
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@Materials_Intel

Materials Intelligence Research @ Harvard

@Materials_Intel
Boris Kozinsky's group at Harvard: Understanding dynamics of materials with computational physics + chemistry and machine learning.
Harvard University, Cambridge MA
mir.g.harvard.edu
Joined March 2019
486 Following
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  • @Materials_Intel
    Materials Intelligence Research @ Harvard
    @Materials_Intel
    Oct 16, 2025
    If you’re in the Boston/Cambridge area on Friday, December 5 (right after the Materials Research Society Fall Meeting), join us at Harvard University for our NequIP Tutorial. 👉 Please RSVP here to attend and receive event updates: docs.google.com/forms/d/e/1FAI…
  • @Materials_Intel
    Materials Intelligence Research @ Harvard
    @Materials_Intel
    Sep 18, 2025
    Excited to share that our NequIP and Allegro foundation potentials, trained by @Kavanagh_Sean_, are up on Matbench Discovery. Check them out at nequip.net ! 🚀
  • @Materials_Intel
    Materials Intelligence Research @ Harvard
    @Materials_Intel
    Jun 11, 2025
    Last month, we released a major update to the NequIP framework that fully leverages PyTorch 2.0 compilation for MLIPs. It’s significantly faster, easier to use, and more versatile than before. Preprint: arxiv.org/abs/2504.16068 Code: github.com/mir-group/nequ… nequip.readthedocs.io
    arXiv logo
    arxiv.org
    High-performance training and inference for deep equivariant...
    Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic...
    1
  • @Materials_Intel
    Materials Intelligence Research @ Harvard
    @Materials_Intel
    Jun 4, 2025
    Our Allegro-Pol model extended the Allegro architecture to predict how materials respond to external electric fields while enforcing physical rules. It could describe vibrational, dielectric, and ferroelectric behavior for systems up to millions of atoms!
    Content cover image
    Unified differentiable learning of electric response
    From nature.com
    1
  • @Materials_Intel
    Materials Intelligence Research @ Harvard
    @Materials_Intel
    Jul 22, 2024
    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! @JPhysChem #IonicLiquids #MachineLearning DOI:
    Issue Cover
    pubs.acs.org
    Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Intera...
    Abstract. Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have bee