Clementi, Cecilia

1. Navigating protein landscapes with a machine-learned transferable coarse-grained model
   2023/10/27 by Nicholas E. Charron, Klara Bonneau, Felix Musil +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
2. 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
3. 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 · 17 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
4. Kinetic distance and kinetic maps from molecular dynamics simulation
   2015/06/20 by Frank Noé, Cecilia Clementi, Noe, Frank +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)
5. Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding
   2025/02/19 by Alessandro Caruso, Jacopo Venturin, Caruso, Alessandro +9 · 3 voices · 9 citations
   Computer Science · Engineering · #Neural Networks and Applications #Advanced Graph Neural Networks #Advanced Memory and Neural Computing
6. Machine learning for protein folding and dynamics
   2019/11/22 by Noé, Frank, De Fabritiis, Gianni, Clementi, Cecilia · 4 citations
   #Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML)
7. 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
8. 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
9. Learning data efficient coarse-grained molecular dynamics from forces and noise
   2024/07/01 by Durumeric, Aleksander E. P., Chen, Yaoyi, Noé, Frank +1 · 5 citations
   #Biological Physics (physics.bio-ph) #Chemical Physics (physics.chem-ph) #FOS: Physical sciences
10. Spectral Properties of Effective Dynamics from Conditional Expectations
    2019/01/06 by Nüske, Feliks, Koltai, Péter, Boninsegna, Lorenzo +1 · 2 citations
    #Biological Physics (physics.bio-ph) #Data Analysis #Dynamical Systems (math.DS) #FOS: Mathematics #FOS: Physical sciences #Probability (math.PR) #Statistics and Probability (physics.data-an)
11. Extensible and Scalable Adaptive Sampling on Supercomputers
    2019/07/16 by Hruska, Eugen, Balasubramanian, Vivekanandan, Lee, Hyungro +2 · 1 citation
    #Computational Physics (physics.comp-ph) #FOS: Biological sciences #FOS: Physical sciences #Quantitative Methods (q-bio.QM)
12. Accurate nuclear quantum statistics on machine-learned classical effective potentials
    2024/07/03 by Iryna Zaporozhets, Félix Musil, Zaporozhets, Iryna +5 · 1 citation
    Computer Science · #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Quantum Computing Algorithms and Architecture