Study on the structural properties of refining slags by molecular dynamics with deep learning potential

Abstract

The CaO-Al2O3 is the most important basis of the multicomponent slag in the steel making industry. The microstructure of molten CaO-Al2O3 systems affects the physical, chemical, and metallurgic properties of refining slags. It is significant to establish a computational method to accurately describe the microstructure of the slag at the atomic level and speculate the properties of large slag models. In this paper, the effect of compositions on the structure of molten CaO-Al2O3 systems was studied by ab initio molecular dynamics, deep learning theory, and deep potential molecular dynamics. The structures of various molten slags were simulated by ab initio molecular dynamics, which accurately describes the interactions between atoms. The potential functions were obtained by deep learning theory. The properties of the large slag models were simulated by molecular dynamics with deep learning potential. As the molar fraction of CaO increases from 0.5 to 0.7, the combination of O and Al mainly forms [AlO4]-5, [AlO3]-3, and [AlO5]-7. Ca presents mainly as free cations in the molten CaO-Al2O3 system. In addition, the diffusion coefficient of Ca decreases from 9.0 × 10-10 m2/s to 5.4 × 10-10 m2/s. The diffusion coefficients of Al and O slightly decrease and are close to 1.1 × 10-10 m2/s and 3.0 × 10-10 m2/s, respectively. It is deduced that the dissolution of CaO provides free O2– and promotes the formation of [AlOn]-b and increases the polymerization degree, which causes deterioration of the fluidity of molten CaO-Al2O3 slags as XCaO increases from 0.5 to 0.7.