Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Abstract

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 Li${}_7$La${}_3$Zr${}_2$O${}_{12}$, tetragonal Li${}_{10}$GeP${}_2$S${}_{12}$, and the $\beta$-eucryptite LiAlSiO${}_4$. 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.

References in corpus (20)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Canonical sampling through velocity-rescaling
• On representing chemical environments
• Precision and efficiency in solid-state pseudopotential calculations
• Comparing molecules and solids across structural and alchemical space
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• Nearsightedness of Electronic Matter
• Analysis of diffusion in solid state electrolytes through MD-simulations, improvement of the Li-ion conductivity in β-Li3PS4 as an example
• Langevin equation with colored noise for constant-temperature molecular dynamics simulations
• Sparse Cyclic Excitations Explain the Low Ionic Conductivity of Stoichiometric Li${}_7$La${}_3$Zr${}_2$O${}_{12}$
• Effects of sublattice symmetry and frustration on ionic transport in garnet solid electrolytes
• Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond
• Hydrogen Dynamics in Superprotonic CsHSO4
• Influence of lattice dynamics on lithium-ion conductivity: A first-principles study
• Accurate thermal conductivities from optimally short molecular dynamics simulations
• Recognizing Local and Global Structural Motifs at the Atomic Scale
• Relationships Between Atomic Diffusion Mechanisms and Ensemble Transport Coefficients in Crystalline Polymorphs
• Modeling lithium-ion solid-state electrolytes with a pinball model
• Dynamical Structure, Bonding, and Thermodynamics of the Superionic Sublattice in alpha-AgI
• Thermal regimes of Li-ion conductivity in beta-eucryptite

Cited by in corpus (9)

• Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
• High-throughput computational screening for solid-state Li-ion conductors
• Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
• Defect-Driven Anomalous Transport in Fast-Ion Conducting Solid Electrolytes
• Li4-xGe1-xPxO4 a potential solid-state electrolyte for all-oxide microbatteries
• Automatic diffusion path exploration for multivalent battery cathodes using geometrical descriptors
• Manifold Learning in Atomistic Simulations: A Conceptual Review
• The solid-state Li-ion conductor Li${}_7$TaO${}_6$: A combined computational and experimental study
• Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression