Phase behavior of a machine-learning potential trained on stress-strain curves: The case of superionic water ice

MMR Zavaroni and F Matusalem and OSC Macollunco and JP Leandro and CJ Ruestes and M de Koning, JOURNAL OF CHEMICAL PHYSICS, 163, 224507 (2025).

DOI: 10.1063/5.0300848

We analyze the transferability of a Deep Potential Machine Learning (DP- ML) model trained to reproduce stress-strain curves of high- temperature/high-pressure crystalline phases of water, determining the coexistence lines for the phase transitions between the insulating ice X and the superionic ice XVIII and that between ice XVIII and its melt. Using a set of various free-energy calculation techniques, we find the resulting coexistence lines to be in good agreement with previous data, indicating that the deformation-trained DP-ML model also transfers to thermodynamic properties. This suggests that the inclusion of deformed solid states in training sets may also be a beneficial general strategy in the development of ML interaction models for other condensed-matter systems. Furthermore, the DP-ML model should be useful to investigate other aspects of the considered phase transitions. One of these involves the possible characterization of the XVIII-liquid transition as weakly first-order, with its potentially associated continuous-like behavior. This is an interesting prospect since it might be the first example of such a transition in a three-dimensional structural solid-liquid transformation.

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