Literature DB >> 26587614

Demonstrating the Transferability and the Descriptive Power of Sketch-Map.

Michele Ceriotti1, Gareth A Tribello2, Michele Parrinello2.   

Abstract

Increasingly, it is recognized that new automated forms of analysis are required to understand the high-dimensional output obtained from atomistic simulations. Recently, we introduced a new dimensionality reduction algorithm, sketch-map, that was designed specifically to work with data from molecular dynamics trajectories. In what follows, we provide more details on how this algorithm works and on how to set its parameters. We also test it on two well-studied Lennard-Jones clusters and show that the coordinates we extract using this algorithm are extremely robust. In particular, we demonstrate that the coordinates constructed for one particular Lennard-Jones cluster can be used to describe the configurations adopted by a second, different cluster and even to tell apart different phases of bulk Lennard-Jonesium.

Year:  2013        PMID: 26587614     DOI: 10.1021/ct3010563

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  15 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

2.  Simulating and analysing configurational landscapes of protein-protein contact formation.

Authors:  Andrej Berg; Christine Peter
Journal:  Interface Focus       Date:  2019-04-19       Impact factor: 3.906

3.  Machine Learning for Electronically Excited States of Molecules.

Authors:  Julia Westermayr; Philipp Marquetand
Journal:  Chem Rev       Date:  2020-11-19       Impact factor: 60.622

4.  Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface.

Authors:  Shichen Lin; Daoling Peng; Weitao Yang; Feng Long Gu; Zhenggang Lan
Journal:  J Chem Phys       Date:  2021-12-07       Impact factor: 3.488

5.  Visualizing energy landscapes with metric disconnectivity graphs.

Authors:  Lewis C Smeeton; Mark T Oakley; Roy L Johnston
Journal:  J Comput Chem       Date:  2014-05-28       Impact factor: 3.376

6.  Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water.

Authors:  Yixing Chen; Halil I Okur; Nikolaos Gomopoulos; Carlos Macias-Romero; Paul S Cremer; Poul B Petersen; Gabriele Tocci; David M Wilkins; Chungwen Liang; Michele Ceriotti; Sylvie Roke
Journal:  Sci Adv       Date:  2016-04-08       Impact factor: 14.136

7.  Mapping and classifying molecules from a high-throughput structural database.

Authors:  Sandip De; Felix Musil; Teresa Ingram; Carsten Baldauf; Michele Ceriotti
Journal:  J Cheminform       Date:  2017-02-02       Impact factor: 5.514

8.  Machine learning for the structure-energy-property landscapes of molecular crystals.

Authors:  Félix Musil; Sandip De; Jack Yang; Joshua E Campbell; Graeme M Day; Michele Ceriotti
Journal:  Chem Sci       Date:  2017-12-12       Impact factor: 9.825

9.  Machine learning unifies the modeling of materials and molecules.

Authors:  Albert P Bartók; Sandip De; Carl Poelking; Noam Bernstein; James R Kermode; Gábor Csányi; Michele Ceriotti
Journal:  Sci Adv       Date:  2017-12-13       Impact factor: 14.136

10.  Chemical shifts in molecular solids by machine learning.

Authors:  Federico M Paruzzo; Albert Hofstetter; Félix Musil; Sandip De; Michele Ceriotti; Lyndon Emsley
Journal:  Nat Commun       Date:  2018-10-29       Impact factor: 14.919

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