ArticleScience advances2021
Exploring order parameters and dynamic processes in disordered systems via variational autoencoders.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Straintronics and Twistronics in 2D Materials: Structural Control and Predictive Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Deep Learning Analysis of Localized Interlayer Stacking Displacement and Dynamics in Bilayer Phosphorene.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy.npj computational materials · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam-induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.
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Registered trials
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