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Found 4 papers, 2 papers with code
Machine learning phase transitions: Connections to the Fisher information
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive.
Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion
Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points.
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
Overall, the prediction accuracy as quantified by the root-mean-squared difference $(\sim 0. 003)$ and the $R^2$ $(\sim 0. 99)$ between the reference QCT and predictions of the STD model is high for the test set and off-grid state specific initial conditions and for initial conditions drawn from reactant state distributions characterized by translational, rotational and vibrational temperatures.
Interpretable and unsupervised phase classification
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest.
Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
