@article{becker_unsupervised_2022-1, abstract = {We propose an unsupervised learning methodology with descriptors based on topological data analysis ({TDA}) concepts to describe the local structural properties of materials at the atomic scale. Based only on atomic positions and without a priori knowledge, our method allows for an autonomous identification of clusters of atomic structures through a Gaussian mixture model. We apply successfully this approach to the analysis of elemental Zr in the crystalline and liquid states as well as homogeneous nucleation events under deep undercooling conditions. This opens the way to deeper and autonomous study of complex phenomena in materials at the atomic scale.}, author = {Becker, Sébastien and Devijver, Emilie and Molinier, Rémi and Jakse, Noël}, date = {2022-04-06}, doi = {10.1103/PhysRevE.105.045304}, journaltitle = {Physical Review E}, keywords = {1 - Crystal structure, 1 - Crystallography, 1 - Material science, 1 - Physics, 2 - Persistent homology, 3 - Molecular dynamic simulation, 3 - atomic configurations, Innovate}, note = {Publisher: American Physical Society}, number = {4}, pages = {045304}, shortjournal = {Phys. Rev. E}, title = {Unsupervised topological learning for identification of atomic structures}, url = {https://link.aps.org/doi/10.1103/PhysRevE.105.045304}, urldate = {2022-12-07}, volume = {105} }