This approach is based on training the potential at high pressures only in the liquid phase and on validating its transferability on the relatively easy-to-calculate cold compression curve. We introduce a relatively simple and inexpensive approach to develop, train, and validate a neural network-based, wide-ranging interatomic potential transferable across both temperature and pressure. Machine learning-based interatomic potentials have shown promise in overcoming this challenge, unlike earlier embedded atom-based approaches. Modeling of phase diagrams and, in particular, the anomalous re-entrant melting curves of alkali metals is an open challenge for interatomic potentials.
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