  Accurate identification of ice phases is essential for understanding various
physicochemical phenomena. However, such classification for structures
simulated with molecular dynamics is complicated by the complex symmetries of
ice polymorphs and thermal fluctuations. For this purpose, both traditional
order parameters and data-driven machine learning approaches have been
employed, but they often rely on expert intuition, specific geometric
information, or large training datasets. In this work, we present an
unsupervised phase classification framework that combines a score-based
denoiser model with a subsequent model-free classification method to accurately
identify ice phases. The denoiser model is trained on perturbed synthetic data
of ideal reference structures, eliminating the need for large datasets and
labeling efforts. The classification step utilizes the Smooth Overlap of Atomic
Positions (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean
symmetries and transferability to various structural systems. Our approach
achieves a remarkable 100\% accuracy in distinguishing ice phases of test
trajectories using only seven ideal reference structures of ice phases as model
inputs. This demonstrates the generalizability of the score-based denoiser
model in facilitating phase identification for complex molecular systems. The
proposed classification strategy can be broadly applied to investigate
structural evolution and phase identification for a wide range of materials,
offering new insights into the fundamental understanding of water and other
complex systems.
