Open Access

Sebastian Falkner, Nadine Schwierz, Pablo Montero de Hijes

• In this work, we employ the isenthalpic–isobaric (NpH) ensemble to sample the transition region of ice nucleation without any external bias, thereby avoiding potentially artificial memory effects introduced by projections onto collective variables. Within this framework, we identify relevant degrees of freedom that expose the intrinsically non-Markovian nature of the largest nucleus size, indicating that it is not sufficient on its own to describe nucleation dynamics. The NpH ensemble leads to long-lived nuclei through the coupling between latent heat release or absorption and temperature fluctuations. As a result, nuclei persist over extended timescales and undergo a slow internal evolution, which we refer to as aging. A signature of this behavior is the emergence of hysteresis in the largest cluster size–temperature plane. To quantify these effects, we perform a structural analysis based on a high-dimensional set of descriptors, which we project onto a low-dimensional latent spaceIn this work, we employ the isenthalpic–isobaric (NpH) ensemble to sample the transition region of ice nucleation without any external bias, thereby avoiding potentially artificial memory effects introduced by projections onto collective variables. Within this framework, we identify relevant degrees of freedom that expose the intrinsically non-Markovian nature of the largest nucleus size, indicating that it is not sufficient on its own to describe nucleation dynamics. The NpH ensemble leads to long-lived nuclei through the coupling between latent heat release or absorption and temperature fluctuations. As a result, nuclei persist over extended timescales and undergo a slow internal evolution, which we refer to as aging. A signature of this behavior is the emergence of hysteresis in the largest cluster size–temperature plane. To quantify these effects, we perform a structural analysis based on a high-dimensional set of descriptors, which we project onto a low-dimensional latent space using a neural network-based autoencoder. This approach reveals the existence of structurally distinct classes of nuclei with similar sizes and temperatures. Finally, we compare the nucleus sizes obtained with this approach with those from previous studies employing different methodologies, finding good agreement.…