Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts
Deep neural network potentials for diffusional lithium isotope fractionation in silicate melts
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DOI：
10.1016/j.gca.2021.03.031
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发表时间：
2021-06
影响因子：
5
通讯作者：
Haiyang Luo;B. Karki;D. Ghosh;H. Bao
中科院分区：
文献类型：
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作者：
Haiyang Luo;B. Karki;D. Ghosh;H. Bao
扩散同位素分馏已被广泛用于解释矿物和岩石中锂同位素的变化。Li扩散的同位素质量依赖性可以经验地表示为D 7 Li D 6 Li= 67 β，其中D是Li同位素的扩散率。对于理解扩散分布和机制至关重要的β因子的温度和成分依赖性的知识仍然不清楚。基于用从头算数据训练的深度神经网络产生的势能和原子间力，我们对钠长石、含水钠长石和模型玄武岩熔体中的几种Li伪同位素（质量= 2、7、21、42 g/mol）进行了深势分子动力学（DPMD）模拟，以评估β因子。我们计算的7锂在钠长石和模型玄武岩熔体在1800 K的扩散系数与实验结果进行比较。发现钠长石熔体的β从4000 K时的0.267±0.006下降到1800 K时的0.225±0.004。含水钠长石熔体中β值从0.250±0.012下降到0.228±0.031，水的存在使β值的温度依赖性略有减弱。模型玄武岩熔体中β的计算值要小得多，从4000 K时的0.215±0.006减小到1800 K时的0.132±0.015。我们对钠长石和含水钠长石熔体β的预测与实验数据符合得很好。更重要的是，我们的研究结果表明，锂同位素在硅酸盐熔体中的扩散是强烈依赖于熔体组成。温度和组成对β的影响可以用离子孔隙率和Li扩散与硅酸盐熔体网络流动性之间的耦合关系来定性解释。两种类型的扩散实验，建议测试我们预测的温度和成分依赖性的β。研究表明，DPMD是模拟元素和同位素在硅酸盐熔体中扩散的一种很有前途的工具。
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 D 7 Li D 6 Li= 6 7 β, 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 7 Li 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.