A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information
Abstract
Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting appropriate descriptors to convert them into time-series, which are then analyzed to extract interpretable information. However, identifying the most effective descriptor is often non-trivial. Here, we report a data-driven approach to compare the efficiency of various descriptors in extracting information from noisy trajectories and translating it into physically relevant insights. As a prototypical system with non-trivial internal complexity, we analyze molecular dynamics trajectories of an atomistic system where ice and water coexist in equilibrium near the solid/liquid transition temperature. We compare general and specific descriptors often used in aqueous systems: number of neighbors, molecular velocities, Smooth Overlap of Atomic Positions (SOAP), Local Environments and Neighbors Shuffling (LENS), Orientational Tetrahedral Order, and distance from the fifth neighbor ($d_5$). Using Onion Clustering -- an efficient unsupervised method for single-point time-series analysis -- we assess the maximum extractable information for each descriptor and rank them via a high-dimensional metric. Our results show that advanced descriptors like SOAP and LENS outperform classical ones due to higher signal-to-noise ratios. Nonetheless, even simple descriptors can rival or exceed advanced ones after local signal denoising. For example, $d_5$, initially among the weakest, becomes the most effective at resolving the system's non-local dynamical complexity after denoising. This work highlights the critical role of noise in information extraction from molecular trajectories and offers a data-driven approach to identify optimal descriptors for systems with characteristic internal complexity.
AI-Generated Overview
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Research Focus: The research investigates a data-driven approach to classify various descriptors based on their effectiveness in converting noisy molecular dynamics trajectories into interpretable, physically relevant information.
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Methodology: The study employs a molecular dynamics simulation of a water-ice system to analyze and compare the performance of various static and dynamic descriptors, utilizing a technique called Onion Clustering for time-series analysis.
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Results: The findings indicate that advanced descriptors like Smooth Overlap of Atomic Positions (SOAP) and Local Environments and Neighbors Shuffling (LENS) are more efficient at extracting useful information from noisy trajectories compared to classical descriptors. However, simple descriptors can also perform well after denoising, with the distance from the fifth neighbor (d5) emerging as particularly effective post-denoising.
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Key Contribution(s): The work introduces a generic, data-driven framework for evaluating descriptor performance in molecular systems, highlighting the critical role of noise reduction techniques in improving descriptor efficiency.
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Significance: This research emphasizes that the choice of descriptors can significantly impact the extraction of information from molecular dynamics data, suggesting that a tailored analysis strategy can yield better insights than relying on a single "best" descriptor.
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Broader Applications: The developed framework and methodology can be applied to various interdisciplinary fields involving noisy time-series data, including materials science and experimental data analysis across multiple scales.