SCiarella/TLS_ML_exploration
Active learning to explore glassy landscapes
This project helps materials scientists and physicists efficiently find rare defects, specifically two-level systems (TLS), within glass simulations. You input simulation data representing pairs of amorphous configurations. The system then outputs predictions about which pairs are likely to be TLS, saving significant computational time by avoiding characterization of uninteresting defects. This is for researchers studying the properties of disordered materials at a microscopic level.
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Use this if you are trying to identify and characterize rare state-to-state transitions, like quantum tunneling two-level systems, in complex systems with slow dynamics and an exponentially large number of states.
Not ideal if your problem does not involve identifying specific transitions between states or if you can easily characterize all possible transitions without machine learning.
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10
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Language
Python
License
MIT
Category
Last pushed
Nov 16, 2024
Commits (30d)
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