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 2.1K Followers 186 Posts

Latest posts 5

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 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 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.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...

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!
nature.com/articles/s4146…

www.nature.com Unified differentiable learning of electric response Nature Communications - The authors introduce a machine-learning framework that predicts how materials respond to electric fields with quantum-level accuracy, capturing vibrational, dielectric, and...

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: doi.org/10.1021/acs.jp…

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