Medium Range Ordering in liquid Al-based alloys: towards a machine learning approach of solidification

Abstract

Abstract Ab initio molecular dynamic simulations of liquid Al93Cr7 and Al83Zn10Cr7 alloy have evidenced the presence of an icosahedral short range order (iSRO) which develops into icosahedral medium range order (iMRO) as the melt is undercooled. This atomic arrangement accounts for the presence of Dynamic Heterogeneities characterized by Al fast-dynamics regions and Cr-rich slow-dynamics regions. Characterisation of the medium range order was carried out by a direct connectivity approach. However, given the small size of the simulation (256 atoms), such characterisation remains partial. In order to better describe both iMRO formation and more dilute alloys closer to industrial compositions, a new modelling strategy has been initiated to allow in the long term for large-scale atomic-level simulations. Molecular Dynamics (MD) of million to billion atoms may indeed lead to meaningful results. Exploitation of such large amounts of MD-generated big data can be carried out by means of Machine Learning (ML) tools which provide relevant and powerful analysis methods. An unsupervised ML approach based on topological descriptors using persistent homology concepts is proposed to reveal the structural features of atomic arrangements without a priori knowledge on the studied system. This approach has been applied so far to pure Al melts. Both translational and orientational orderings are thus evidenced together with nucleation pathways, whose revealed features are beyond the hypotheses of the Classical Nucleation Theory.