Demonstrating the Transferability and the Descriptive Power of Sketch-Map.
By Michele Ceriotti, Gareth A. Tribello, Michele Parrinello. 2013
112 citations recorded by the source corpus.
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.
Subfields: Condensed Matter Physics, Materials Chemistry, Molecular Biology
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In Condensed Matter Physics
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Citations in the corpus
- Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. (2018, methodology): “Possible approaches include Farthest Point Sampling (FPS), 32,33 to maximize the dissimilarity of the inputs in the representative set, and the use of CUR to minimize the effect of the PP approximation on the kernel matrix of the entire training set.”