Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts

Abstract

Diffusional isotope fractionation has been widely used to explain lithium (Li) isotope variations in minerals and rocks. Isotopic mass dependence of Li diffusion can be empirically expressed as D7LiD6Li=67β, where D is the diffusivity of a Li isotope. The knowledge about temperature and compositional dependence of the β factor which is essential for understanding diffusion profiles and mechanisms remains unclear. Based on the potential energy and interatomic forces generated by deep neural networks trained with ab initio data, we performed deep potential molecular dynamics (DPMD) simulations of several Li pseudo-isotopes (with mass = 2, 7, 21, 42 g/mol) in albite, hydrous albite, and model basalt melts to evaluate the β factor. Our calculated diffusivities for 7Li in albite and model basalt melts at 1800 K compare well with experimental results. We found that β in albite melt decreases from 0.267±0.006 at 4000 K to 0.225±0.004 at 1800 K. The presence of water appears to slightly weaken the temperature dependence of β, with β decreasing from 0.250±0.012 to 0.228±0.031 in hydrous albite melt. The calculated β in model basalt melt takes much smaller values, decreasing from 0.215±0.006 at 4000 K to 0.132±0.015 at 1800 K. Our prediction of β in albite and hydrous albite melts is in good agreement with experimental data. More importantly, our results suggest that Li isotope diffusion in silicate melts is strongly dependent on melt composition. The temperature and compositional effects on β can be qualitatively explained in terms of ionic porosity and the coupled relationship between Li diffusion and the mobility of the silicate melt network. Two types of diffusion experiments are suggested to test our predicted temperature and compositional dependence of β. This study shows that DPMD is a promising tool to simulate the diffusion of elements and isotopes in silicate melts.