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

María Milagros Raimondo Zavaroni,Filipe Matusalem,Oscar Samuel Cajahuaringa Macollunco,Julia Perretto Leandro,C. Ruestes,M. de Koning

Published 2025 in Journal of Chemical Physics

ABSTRACT

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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