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DADApy is a python software package for analysing and characterising high-dimensional data manifolds.
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R. Capelli, A. Gardin, C. Empereur-Mot, G. Doni, G. Pavan, A data-driven dimensionality reduction approach to compare and classify lipid force fields, The Journal of Physical Chemistry B 125 (28) (2021) 7785–7796
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arXiv:https://doi.org/10.1021/acs.jctc.1c00586
F. Marinelli, J. D. Faraldo-Gómez, Force-correction analysis method for derivation of multidimensional free-energy landscapes from adaptively biased replica simulations , Journal of Chemical Theory and Computation 17 (11) (2021) 6775–6788, pMID: 34669402 · 2021
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M. Carli, A. Laio, Statistically unbiased free energy estimates from biased simulations, Molecular Physics 119 (19-20) (2021) e1899323
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M. d’Errico, E. Facco, A. Laio, A. Rodriguez, Automatic topography of high-dimensional data sets by non-parametric density peak clustering, Information Sciences 560 (2021) 476–492
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G. Margazoglou, T. Grafke, A. Laio, V. Lucarini, Dynamical landscape and multistability of a climate model, Proceedings of the Royal Society A 477 (2250) (2021) 20210019
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D. R. Salahub, Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond , Phys. Chem. Chem. Phys. 24 (2022) 9051–9081 · 2022
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arXiv:https://doi.org/10.1021/acs.jctc.1c01292
A. Offei-Danso, A. Hassanali, A. Rodriguez, High-dimensional fluctuations in liquid water: Combining chemical intuition with unsupervised learning , Journal of Chemical Theory and Computation 18 (5) (2022) 3136–3150, pMID: 35472272 · 2022
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C. Zeni, A. Anelli, A. Glielmo, K. Rossi, Exploring the robust extrapolation of high-dimensional machine learning potentials, Physical Review B 105 (16) (2022) 165141
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A. Glielmo, C. Zeni, B. Cheng, G. Csányi, A. Laio, Ranking the information content of distance measures , PNAS Nexus 1 (2), pgac039 (04 2022) · 2022
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