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Number of items: 39. Hoffmann, Moritz and Scherer, Martin and Hempel, Tim and Mardt, Andreas and de Silva, Brian and Husic, Brooke E. and Klus, Stefan and Wu, Hao and Kutz, Nathan and Brunton, Steven L (2021) Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn.: Sci. Technol. 3 (2022), 3 (015009). pp. 1-28. Kostré, Margarita and Schütte, Christof and Noé, Frank and del Razo, Mauricio J. (2021) Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs. Sociaty for Industrial and Applied Mathematics, 19 (4). del Razo, Mauricio J. and Dibak, Manuel and Schütte, Christof and Noé, Frank (2021) Multiscale molecular kinetics by coupling Markov state models and reaction-diffusion dynamics. The Journal of Chemical Physics, 155 (12). Chen, Yaoyi and Krämer, Andreas and Charron, Nicholas E. and Husic, Brooke E. and Clementi, Cecilia and Noé, Frank (2021) Machine learning implicit solvation for molecular dynamics. The Journal of Chemical Physics, 155 (084101). pp. 1-15. Klus, Stefan and Gelß, Patrick and Nüske, Feliks and Noé, Frank (2021) Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Machine Learning: Science and Technologie, 2 . pp. 1-23. Husic, Brooke E. and Charron, Nicholas E. and Lemm, Dominik and Wang, Jiang and Pérez, Adrià and Majewski, Maciej and Krämer, Andreas and Chen, Yaoyi and Olsson, Simon and de Fabritiis, Gianni and Noé, Frank and Clementi, Cecilia (2020) Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys., 153 (194101). pp. 1-17. Noé, Frank (2020) Machine Learning for Molecular Dynamics on Long Timescales. Machine Learning Meets Quantum Physics . pp. 331-372. Noé, Frank and Tkatchenko, Alexandre and Müller, Klaus-Robert and Clementi, Cecilia (2020) Machine Learning for Molecular Simulation. Annual Review of Physical Chemistry, 71 . pp. 361-390. Hartmann, Carsten and Neureither, Lara and Sharma, Upanshu (2020) Coarse-graining of non-reversible stochasticdifferential equations: quantitative results and connections to averaging. SIAM Journal on Numerical Analysis, 52 (3). pp. 2689-2733. ISSN 0036-1429 Mardt, Andreas and Pasquali, Luca and Noé, F. and Wu, Hao (2020) Deep learning Markov and Koopman models with physical constraints. Proceedings of Machine Learning Research, 107 . pp. 451-475. Klus, Stefan and Husic, Brooke E. and Mollenhauer, Mattes and Noé, Frank (2019) Kernel methods for detecting coherent structures in dynamical data. Chaos, 29 (12). Noé, F. and Olsson, S. and Köhler, J. and Wu, H. (2019) Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning. Science, 365 (6457). eaaw1147. Wu, Ho and Noé, Frank (2019) Variational approach for learning Markov processes from time series data. Journal of Nonlinear Science, 30 . pp. 23-66. ISSN 1432-1467 (online) Nüske, Feliks and Boninsegna, Lorenzo and Clementi, Cecilia (2019) Coarse-graining molecular systems by spectral matching. J. Chem. Phys., 151 . pp. 1-10. ISSN 0021-9606, ESSN: 1089-7690 Wang, J. and Olsson, S. and Wehmeyer, C. and Perez, A. and Charron, N.E. and de Fabritiis, G. and Noé, F. and Clementi, C. (2019) Machine Learning of coarse-grained Molecular Dynamics Force Fields. ACS Cent. Sci., 5 (5). pp. 755-767. ISSN 2374-7943, ESSN: 2374-7951 Schulz, R. and von Hansen, Y. and Daldrop, J.O. and Kappler, J. and Noé, F. and Netz, R.R. (2018) Collective hydrogen-bond rearrangement dynamics in liquid water. J. Chem. Phys., 149 (24). -244504. ISSN 0021-9606, ESSN: 1089-7690 Scherer, M. K. and Husic, B.E. and Hoffmann, M. and Paul, F. and Wu, H. and Noé, F. (2018) Variational Selection of Features for Molecular Kinetics. SFB 1114 Preprint in arXiv:1811.11714 . pp. 1-12. (Unpublished) Wehmeyer, C. and Scherer, M. K. and Hempel, T. and Husic, B.E. and Olsson, S. and Noé, F. (2018) Introduction to Markov state modeling with the PyEMMA software — v1.0. LiveCoMS, 1 (1). pp. 1-12. ISSN E-ISSN: 2575-6524 Swenson, D.W.H. and Prinz, J.-H. and Noé, F. and Chodera, J. D. and Bolhuis, P.G. (2018) OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics. Journal of Chemical Theory and Computation, Article ASAP . ISSN 1549-9618, ESSN: 15-49-9626 Swenson, D.W.H. and Prinz, J.-H. and Noé, F. and Chodera, J. D. and Bolhuis, P.G. (2018) OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes. Journal of Chemical Theory and Computation, Article ASAP . ISSN 1549-9618, ESSN: 15-49-9626 del Razo, M.J. and Qian, H. and Noé, F. (2018) Grand canonical diffusion-influenced reactions: a stochastic theory with applications to multiscale reaction-diffusion simulations. J. Chem. Phys., 149 (4). 044102. ISSN 0021-9606, ESSN: 1089-7690 Dibak, M. and del Razo, M.J. and De Sancho, D. and Schütte, Ch. and Noé, F. (2018) MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations. Journal of Chemical Physics, 148 (214107). ISSN 0021-9606 Koltai, P. and Wu, H. and Noé, F. and Schütte, Ch. (2018) Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics. Computation, 6(1) (22). ISSN 2079-3197 (online) Klus, S. and Nüske, F. and Koltai, P. and Wu, H. and Kevrekidis, I. and Schütte, Ch. and Noé, F. (2018) Data-driven model reduction and transfer operator approximation. Journal of Nonlinear Science, 28 (1). pp. 1-26. Paul, F. and Noé, F. and Weikl, T. (2018) Identifying Conformational-Selection and Induced-Fit Aspects in the Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations. J. Phys. Chem. B . Paul, F. and Wehmeyer, C. and Abualrous, E. T. and Wu, H. and Crabtree, M. D. and Schöneberg, J. and Clarke, J. and Freund, C. and Weikl, T. and Noé, F. (2017) Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations. Nat. Comm., 8 (1095). Gerber, S. and Horenko, I. (2017) Toward a direct and scalable identification of reduced models for categorical processes. Proceedings of the National Academy of Sciences, 114 (19). pp. 4863-4868. Nüske, F. and Wu, H. and Wehmeyer, C. and Clementi, C. and Noé, F. (2017) Markov State Models from short non-Equilibrium Simulations - Analysis and Correction of Estimation Bias. J. Chem. Phys., 146 . 094104. Olsson, Simon and Wu, H. and Paul, F. and Clementi, C. and Noé, F. (2017) Combining experimental and simulation data of molecular processes via augmented Markov models. Proc. Natl. Acad. Sci. USA, 114 . pp. 8265-8270. Wu, H. and Paul, F. and Wehmeyer, C. and Noé, F. (2016) Multiensemble Markov models of molecular thermodynamics and kinetics. Proceedings of the National Academy of Sciences, 113 (23). E3221-E3230 . ISSN 0027-8424 Nüske, F. and Schneider, R. and Vitalini, F. and Noé, F. (2016) Variational Tensor Approach for Approximating the Rare-Event Kinetics of Macromolecular Systems. J. Chem. Phys., 144 (5). 054105. Paul, F. and Weikl, T. (2016) How to Distinguish Conformational Selection and Induced Fit Based on Chemical Relaxation Rates. PLOS Computational Biology . Vitalini, F. and Noé, F. and Keller, B. (2016) Molecular dynamics simulations data of the twenty encoded amino acids in different force fields. Data in Brief, 7 . pp. 582-590. Trendelkamp-Schroer, B. and Wu, H. and Paul, F. and Noé, F. (2015) Estimation and uncertainty of reversible Markov models. J. Chem. Phys., 143 (17). p. 174101. Scherer, M. K. and Trendelkamp-Schroer, B. and Paul, F. and Pérez-Hernández, G. and Hoffmann, M. and Plattner, N. and Wehmeyer, C. and Prinz, J.-H. and Noé, F. (2015) PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models. J. Chem. Theory Comput., 11 (11). pp. 5525-5542. Wu, H. and Prinz, J.-H. and Noé, F. (2015) Projected Metastable Markov Processes and Their Estimation with Observable Operator Models. J. Chem. Phys., 143 (14). p. 144101. Wu, H. and Noé, F. (2015) Gaussian Markov transition models of molecular kinetics. J. Chem. Phys., 142 (8). 084104. Wu, H. and Mey, A.S.J.S. and Rosta, E. and Noé, F. (2014) Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states. J. Chem. Phys., 141 (21). p. 214106. Mey, A.S.J.S. and Wu, H. and Noé, F. (2014) xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states. Phys. Rev. X, 4 (4). 041018. |