Simon Batzner

Harvard University — United States

Materials Science · Physical Sciences

14h-index3.6kcitations40works21.02yr avg
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Research Topics

Machine Learning in Materials Science(32), Protein Structure and Dynamics(11), Topic Modeling(5), Computational Drug Discovery Methods(5), X-ray Diffraction in Crystallography(5)

Publications40 total

The design space of E(3)-equivariant atom-centred interatomic potentials
Nature Machine Intelligence·2025· 118 citedOpen Access
Predicting emergence of crystals from amorphous precursors with deep learning potentials
Nature Computational Science·2024· 10 citedOpen Access
Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions
Journal of the American Chemical Society·2024· 4 citedOpen Access
Generative Hierarchical Materials Search
arXiv (Cornell University)·2024· 1 citedOpen Access
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
The Journal of Physical Chemistry Letters·2024· 26 cited
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
npj Computational Materials·2024· 34 citedOpen Access
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
arXiv (Cornell University)·2024Open Access
Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials
ACS Omega·2024· 16 citedOpen Access
Generative Hierarchical Materials Search
2024· 1 cited
LiPS
TIB Data Manager·2024Open Access
Scaling deep learning for materials discovery
Nature·2023· 989 citedOpen Access
Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size
2023· 40 citedOpen Access
Predicting emergence of crystals from amorphous matter with deep learning
arXiv (Cornell University)·2023· 3 citedOpen Access
Advancing molecular simulation with equivariant interatomic potentials
Nature Reviews Physics·2023· 28 cited
Fast uncertainty estimates in deep learning interatomic potentials
The Journal of Chemical Physics·2023· 63 citedOpen Access
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
arXiv (Cornell University)·2023· 1 citedOpen Access
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
arXiv (Cornell University)·2023· 14 citedOpen Access
Biasing energy surfaces towards the unknown
Nature Computational Science·2023· 6 cited
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
arXiv (Cornell University)·2023· 2 citedOpen Access
Learning local equivariant representations for large-scale atomistic dynamics
Nature Communications·2023· 545 citedOpen Access
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Frequent Co-authors

Boris Kozinsky(29), Albert Musaelian(16), Lixin Sun(13), Jonathan Vandermause(9), Ekin D. Cubuk(8), Steven B. Torrisi(8), Muratahan Aykol(6), Yu Xie(6), Mordechai Kornbluth(5), Tess Smidt(5), Alexie M. Kolpak(5), Nicola Molinari(4), Amil Merchant(4), Cameron J. Owen(4), Lixin Sun(4), Mario Geiger(3), Jonathan P. Mailoa(3), Anders Johansson(3), Wei Chen(3), Kyle Bystrom(3)