Predicting events in metastable systems is an important but challenging problem. It can help society forecast, prevent, or prepare for upcoming catastrophes. However, many metastable systems in nature operate near a critical point and are empirically unpredictable. We developed machine learning predictors, applied them to the prediction of nucleation events in the metastable Ising model, near and far from the spinodal critical point. We observed decreasing predictability as the critical point is approached, and found that this unpredictability is due to the vanishing density difference between the nucleating droplet and the background. We also developed a tensor representation of Lennard-Jones con gurations using the symmetry order parameters of the particles and use this representation to predict nucleation in a dense Lennard Jones liquid. Finally, we investigated the noise-induced critical point in two variations of the OFC model - a coupled OFC model and a OFC model with multiplicative noise. In both variations, we found a critical phase boundary that separates the ergodic and non-ergodic phase and the termination point of the phase boundary, which is consistent with a higher-order phase transition.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43927 |
| Date | 24 February 2022 |
| Creators | Huang, Shan |
| Contributors | Klein, William |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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