Musaelian, Albert
- Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
2022/04/11 by Albert Musaelian, Simon Batzner, Musaelian, Albert +11 · 77 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, Simon Batzner, Batatia, Ilyes +15 · 61 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
- Unified Differentiable Learning of Electric Response
2024/03/25 by Falletta, Stefano, Cepellotti, Andrea, Johansson, Anders +4 · 11 citations
#FOS: Physical sciences #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)
- 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
- High-performance training and inference for deep equivariant interatomic potentials
2025/04/22 by Tan, Chuin Wei, Descoteaux, Marc L., Kotak, Mit +11 · 13 citations
#Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
- 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)
- Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
2024/05/29 by Duschatko, Blake R., Fu, Xiang, Owen, Cameron +4 · 3 citations
#Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences
- Learning Interatomic Potentials at Multiple Scales
2023/10/20 by Fu, Xiang, Musaelian, Albert, Johansson, Anders +2 · 2 citations
#Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
- Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
2024/03/04 by Zachary A. H. Goodwin, Goodwin, Zachary A. H., Malia B. Wenny +23 · 2 citations
Chemical Engineering · Chemistry · Engineering · #Ionic liquids properties and applications #Electrochemical Analysis and Applications #Advanced Chemical Sensor Technologies
- Atomistic evolution of active sites in multi-component heterogeneous catalysts
2024/07/18 by Owen, Cameron J., Russotto, Lorenzo, O'Connor, Christopher R. +4 · 1 citation
#Applied Physics (physics.app-ph) #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)