Boris Kozinsky

1. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
   2022/05/04 by Simon Batzner, Albert Musaelian, Lixin Sun +6 · 230 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Machine Learning in Materials Science #Protein Structure and Dynamics #Topic Modeling
2. Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
   2022/04/11 by Albert Musaelian, Musaelian, Albert, Simon Batzner +11 · 97 citations
   Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Topic Modeling #Protein Structure and Dynamics
3. The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
   2022/05/13 by Ilyes Batatia, Simon Batzner, Batatia, Ilyes +15 · 74 citations
   Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography
4. On-the-Fly Active Learning of Interpretable Bayesian Force Fields for\n Atomistic Rare Events
   2019/04/03 by Jonathan Vandermause, Vandermause, Jonathan, Steven B. Torrisi +11 · 23 citations
   Computer Science · Materials Science · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning and Algorithms #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
5. Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
   2023/04/20 by Albert Musaelian, Anders Johansson, Musaelian, Albert +5 · 13 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Parallel Computing and Optimization Techniques #Protein Structure and Dynamics
6. Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC
   2022/03/08 by Yu Xie, Xie, Yu, Jonathan Vandermause +9 · 9 citations
   Computer Science · Materials Science · #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning and Algorithms #Machine Learning in Materials Science
7. Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
   2023/02/25 by Cameron J. Owen, Steven B. Torrisi, Owen, Cameron J. +14 · 8 citations
   Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Applied Physics (physics.app-ph) #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Protein Structure and Dynamics
8. Multitask machine learning of collective variables for enhanced sampling of rare events
   2020/12/07 by Lixin Sun, Jonathan Vandermause, Sun, Lixin +11 · 5 citations
   Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantum many-body systems
9. Uncertainty Driven Active Learning of Coarse Grained Free Energy Models
   2022/10/28 by Blake Duschatko, Jonathan Vandermause, Duschatko, Blake R. +5 · 4 citations
   Biochemistry, Genetics and Molecular Biology · Materials Science · #Block Copolymer Self-Assembly #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Protein Structure and Dynamics
10. A Recipe for Charge Density Prediction
    2024/05/29 by Xiang Fu, Andrew Rosen, Fu, Xiang +13 · 6 citations
    Materials Science · #Computational Physics (physics.comp-ph) #Electron and X-Ray Spectroscopy Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
11. Charge ordering and hopping in a triangular array of quantum dots
    1999/12/30 by Leonid Levitov, L. S. Levitov, Levitov, L. S. +2 · 1 citation
    Physics and Astronomy · #FOS: Physical sciences #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Quantum and electron transport phenomena #Quantum many-body systems #Semiconductor Quantum Structures and Devices #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.mes-hall #cond-mat.stat-mech
12. Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
    2024/03/04 by Zachary A. H. Goodwin, Malia B. Wenny, Goodwin, Zachary A. H. +23 · 4 citations
    Chemical Engineering · Chemistry · Engineering · #Ionic liquids properties and applications #Electrochemical Analysis and Applications #Advanced Chemical Sensor Technologies
13. Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
    2024/05/29 by Blake Duschatko, Xiang Fu, Duschatko, Blake R. +11 · 3 citations
    Engineering · #Advanced Surface Polishing Techniques #Advanced machining processes and optimization #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Mineral Processing and Grinding
14. Theory of Cation Solvation and Ionic Association in Non-Aqueous Solvent Mixtures
    2023/01/19 by Zachary A. H. Goodwin, Goodwin, Zachary A. H., Michael McEldrew +5 · 2 citations
    Physics and Astronomy · Chemical Engineering · Chemistry · #Spectroscopy and Quantum Chemical Studies #Ionic liquids properties and applications #Electrochemical Analysis and Applications
15. Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
    2023/04/24 by Mgcini Keith Phuthi, Archie Mingze Yao, Phuthi, Mgcini Keith +11 · 2 citations
    Engineering · Materials Science · #Advanced Battery Materials and Technologies #Advancements in Battery Materials #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
16. Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
    2024/01/10 by Jonathan Vandermause, Anders Johansson, Vandermause, Jonathan +7 · 1 citation
    Materials Science · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Force Microscopy Techniques and Applications #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Shape Memory Alloy Transformations
17. Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
    2025/02/25 by Yu Xie, M. J. Wang, Xie, Yu +7 · 2 citations
    Materials Science · Engineering · #Machine Learning in Materials Science #Silicon Carbide Semiconductor Technologies #Boron and Carbon Nanomaterials Research
18. Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials
    2026/07/30 by Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
    Physics and Astronomy · #physics.comp-ph #cond-mat.mtrl-sci #physics.app-ph #physics.chem-ph