Ordered ground state configurations of the asymmetric Wigner bilayer system -- revisited: an unsupervised clustering algorithm analysis
Benedikt Hartl, Marek Mihalkovi\v{c}, Ladislav \v{S}amaj, Martial, Mazars, Emmanuel Trizac, Gerhard Kahl

TL;DR
This study applies unsupervised machine learning techniques to re-analyze and refine the classification of ground state configurations in the asymmetric Wigner bilayer system, revealing new structures and improving understanding.
Contribution
It introduces a systematic, machine learning-based approach using PCA and k-means clustering to classify complex ground states, surpassing previous manual methods.
Findings
Identification of new ground state structures
Refinement of the phase diagram classification
Validation confirms the robustness of the new scheme
Abstract
We have re-analysed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [M. Antlanger \textit{et al.}, Phys. Rev. Lett. \textbf{117}, 118002 (2016)], comprising roughly 60~000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out ``by hand'', we have used this time machine learning concepts, notably based on a principal component analysis and a -means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters) we were able to re-analyse these ground state…
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Taxonomy
TopicsMachine Learning in Materials Science · Cold Atom Physics and Bose-Einstein Condensates · Spectroscopy and Quantum Chemical Studies
