Cecilia Clementi
- Machine Learning for Molecular Simulation
2020/02/24 by Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller +1 · 49 citations
Materials Science · Physics and Astronomy · #Block Copolymer Self-Assembly #Machine Learning in Materials Science #Quantum many-body systems
- Coarse graining molecular dynamics with graph neural networks
2020/11/16 by Brooke E. Husic, Nicholas E. Charron, Dominik Lemm +9 · 49 citations
Materials Science · Engineering · #Machine Learning in Materials Science #Block Copolymer Self-Assembly #Surface Chemistry and Catalysis
- Scalable emulation of protein equilibrium ensembles with generative deep learning
2024/12/05 by Sarah Lewis, Tim Hempel, José Jiménez-Luna +23 · 2 voices · 62 citations
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Protein Structure and Dynamics #Scientific Computing and Data Management
- Navigating protein landscapes with a machine-learned transferable coarse-grained model
2023/10/27 by Nicholas E. Charron, Felix Musil, Charron, Nicholas E. +40 · 3 voices · 27 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
- Machine Learning of coarse-grained Molecular Dynamics Force Fields
2018/12/04 by Jiang Wang, Wang, Jiang, Simon Olsson +13 · 16 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Protein Structure and Dynamics
- Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
2023/02/01 by Marloes Arts, Arts, Marloes, Víctor García Satorras +15 · 16 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics #Topic Modeling
- Multi-body effects in a coarse-grained protein force field
2021/04/28 by Jiang Wang, Nicholas Charron, Nicholas E. Charron +5 · 29 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Machine Learning in Materials Science #Enzyme Structure and Function
- Kinetic distance and kinetic maps from molecular dynamics simulation
2015/06/20 by Frank Noé, Noe, Frank, Cecilia Clementi +1 · 6 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #Blind Source Separation Techniques #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #Data Analysis #Electrochemical Analysis and Applications #FOS: Biological sciences #FOS: Physical sciences #Protein Structure and Dynamics #Statistics and Probability (physics.data-an)
- Coarse-grained models of protein folding: toy models or predictive tools?
2007/12/26 by Cecilia Clementi · 6 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Physics and Astronomy · #Enzyme Structure and Function #Force Microscopy Techniques and Applications #Protein Structure and Dynamics
- Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding
2025/02/19 by Alessandro Caruso, Caruso, Alessandro, Jacopo Venturin +9 · 3 voices · 9 citations
Computer Science · Engineering · #Neural Networks and Applications #Advanced Graph Neural Networks #Advanced Memory and Neural Computing
- Topological and energetic factors: what determines the structural details of the transition state ensemble and "on-route" intermediates for protein folding? An investigation for small globular proteins
2000/03/28 by Cecilia Clementi, Clementi, Cecilia, Hugh Nymeyer +4 · 2 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Physics and Astronomy · #Enzyme Structure and Function #FOS: Biological sciences #FOS: Physical sciences #Protein Structure and Dynamics #Quantitative Biology (q-bio) #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #q-bio
- Machine learning coarse-grained potentials of protein thermodynamics
2023/09/15 by Maciej Majewski, Adrià Pérez, Philipp Thölke +7 · 1 voice · 5 citations
Materials Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Protein Structure and Dynamics #Enzyme Structure and Function
- Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
2023/02/14 by Andreas Krämer, Aleksander P. Durumeric, Krämer, Andreas +9 · 4 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Biological Physics (physics.bio-ph) #Block Copolymer Self-Assembly #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Machine Learning in Materials Science #Protein Structure and Dynamics
- Accurate nuclear quantum statistics on machine-learned classical effective potentials
2024/07/03 by Iryna Zaporozhets, Zaporozhets, Iryna, Félix Musil +5 · 1 citation
Computer Science · #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Quantum Computing Algorithms and Architecture