Unsupervised landmark analysis for jump detection in molecular dynamics simulations
- Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
DOI10.24435/materialscloud:2019.0008/v1 (version v1, submitted on 12 February 2019)
How to cite this entry
Leonid Kahle, Albert Musaelian, Nicola Marzari, Boris Kozinsky, Unsupervised landmark analysis for jump detection in molecular dynamics simulations, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0008/v1.
Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the Smooth Overlap of Atomic Positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li7La3Zr2O12, tetragonal Li10GeP2S12, and the β-eucryptite LiAlSiO4. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated.
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|936 Bytes||The README contains information on the notebooks and some guidance on installing the necessary packages.|
|1.2 GiB||The compressed file contains two jupyter notebooks, and a subfolder with the necessary data, namely two molecular dynamics trajectories. The trajectories are saved in a custom format, but the raw data is also available in xyz-format.|
12 February 2019 [This version]