Batzner, Simon
- Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
2022/04/11 by Albert Musaelian, Simon Batzner, Musaelian, Albert +11 · 79 citations
Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Topic Modeling #Protein Structure and Dynamics
- The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
2022/05/13 by Ilyes Batatia, Batatia, Ilyes, Simon Batzner +15 · 62 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
- 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 · 16 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)
- Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
2023/04/20 by Musaelian, Albert, Johansson, Anders, Batzner, Simon +1 · 9 citations
#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)
- Multitask machine learning of collective variables for enhanced sampling of rare events
2020/12/07 by Lixin Sun, Sun, Lixin, Jonathan Vandermause +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
- 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, Owen, Cameron J., Steven B. Torrisi +14 · 5 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
- Generative Hierarchical Materials Search
2024/09/10 by Sherry Yang, Simon Batzner, Yang, Sherry +17 · 5 citations
Engineering · Materials Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Manufacturing Process and Optimization #Materials Science (cond-mat.mtrl-sci)
- 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 · 2 citations
Chemical Engineering · Chemistry · Engineering · #Ionic liquids properties and applications #Electrochemical Analysis and Applications #Advanced Chemical Sensor Technologies
- 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, Phuthi, Mgcini Keith, Archie Mingze Yao +11 · 1 citation
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)