Simon Olsson
- Coarse graining molecular dynamics with graph neural networks
2020/11/16 by Brooke E. Husic, Nicholas E. Charron, Dominik Lemm +9 · 46 citations
Materials Science · Engineering · #Machine Learning in Materials Science #Block Copolymer Self-Assembly #Surface Chemistry and Catalysis
- Navigating protein landscapes with a machine-learned transferable coarse-grained model
2023/10/27 by Nicholas E. Charron, Felix Musil, Klara Bonneau +40 · 3 voices · 23 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Mathematics · Physics and Astronomy · #Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #Block Copolymer Self-Assembly #Chemical Physics (physics.chem-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Machine Learning in Materials Science #Protein Structure and Dynamics #physics.bio-ph #physics.chem-ph #q-bio.BM #stat.ML
- Multi-body effects in a coarse-grained protein force field
2021/04/28 by Jiang Wang, Nicholas Charron, Nicholas E. Charron +5 · 28 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Machine Learning in Materials Science #Enzyme Structure and Function
- SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
2024/06/11 by Ross W. Irwin, Ross Irwin, Alessandro Tibo +7 · 2 voices · 7 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning (cs.LG) #Microfluidic and Capillary Electrophoresis Applications #Nanopore and Nanochannel Transport Studies #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.LG #cs.NE
- Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
2023/05/29 by Mathias Schreiner, Schreiner, Mathias, Ole Winther +3 · 6 citations
Materials Science · Biochemistry, Genetics and Molecular Biology · Computer Science · #Machine Learning in Materials Science #Protein Structure and Dynamics #Gaussian Processes and Bayesian Inference
- Boltzmann priors for Implicit Transfer Operators
2024/10/14 by Juan Viguera Diez, Diez, Juan Viguera, Mathias Schreiner +5 · 1 voice · 7 citations
Physics and Astronomy · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #physics.chem-ph
- Thermodynamic Interpolation: A generative approach to molecular thermodynamics and kinetics
2024/11/15 by Selma Moqvist, Weilong Chen, Moqvist, Selma +7 · 6 citations
Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Chemical Physics (physics.chem-ph) #FOS: Physical sciences
- Markov Field Models: scaling molecular kinetics approaches to large molecular machines
2022/06/23 by Tim Hempel, Hempel, Tim, Simon Olsson +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #Biological Physics (physics.bio-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Protein Structure and Dynamics
- Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
2025/10/08 by Juan Viguera Diez, Mathias Schreiner, Diez, Juan Viguera +3 · 1 voice · 4 citations
Mathematics · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #physics.chem-ph #stat.ML
- Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
2018/12/04 by Frank Noé, Simon Olsson, Noé, Frank +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Protein Structure and Dynamics #Statistical Mechanics (cond-mat.stat-mech)
- Boltzmann-Expected Molecular Design with Decoupled Annealing Flows
2026/07/21 by Selma Moqvist, Richard Beckmann, Ross Irwin +2
#stat.ML #cs.LG