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首页> 外文期刊>Acta Crystallographica, Section B. Structural science, crystal engineering and materials >SoftBV - a software tool for screening the materials genome of inorganic fast ion conductors
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SoftBV - a software tool for screening the materials genome of inorganic fast ion conductors

机译：SOFTBV - 用于筛选无机快速离子导线材料基因组的软件工具

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The identification of materials for advanced energy-storage systems is still mostly based on experimental trial and error. Increasingly, computational tools are sought to accelerate materials discovery by computational predictions. Here are introduced a set of computationally inexpensive software tools that exploit the bond-valence-based empirical force field previously developed by the authors to enable high-throughput computational screening of experimental or simulated crystal-structure models of battery materials predicting a variety of properties of technological relevance, including a structure plausibility check, surface energies, an inventory of equilibrium and interstitial sites, the topology of ion-migration paths in between those sites, the respective migration barriers and the site-specific attempt frequencies. All of these can be predicted from CIF files of structure models at a minute fraction of the computational cost of density functional theory (DFT) simulations, and with the added advantage that all the relevant pathway segments are analysed instead of arbitrarily predetermined paths. The capabilities and limitations of the approach are evaluated for a wide range of ion-conducting solids. An integrated simple kinetic Monte Carlo simulation provides rough (but less reliable) predictions of the absolute conductivity at a given temperature. The automated adaptation of the force field to the composition and charge distribution in the simulated material allows for a high transferability of the force field within a wide range of Lewis acid-Lewis base-type ionic inorganic compounds as necessary for high-throughput screening. While the transferability and precision will not reach the same levels as in DFT simulations, the fact that the computational cost is several orders of magnitude lower allows the application of the approach not only to pre-screen databases of simple structure prototypes but also to structure models of complex disordered or amorphous p
机译：用于高级能量储存系统的材料的识别主要是基于实验试验和误差。越来越多地通过计算预测来加速计算工具来加速材料发现。这里介绍了一组计算廉价的软件工具，利用了先前由作者开发的基于键合的实证力领域，以实现预测各种性质的电池材料的实验或模拟晶体结构模型的高通量计算筛选技术相关性，包括结构合理性检查，表面能，均衡和间隙位点，这些部位之间的离子迁移路径的拓扑，各个迁移障碍和特定于场地的尝试频率。所有这些都可以从结构模型的CIF文件预测到密度泛函理论（DFT）模拟的计算成本的一分钟，并且附加的优点是，分析所有相关的通路段代替任意预定路径。对方法的能力和局限性评估了各种离子导电固体。集成的简单动力学蒙特卡罗模拟在给定温度下提供了对绝对电导率的粗糙（但不太可靠）预测。在模拟材料中的组合物和电荷分布的自动适应力允许在高通量筛选的必要方案中的宽范围内的路易斯酸-lewis基型离子无机化合物内的高可转移性。虽然可转换性和精度不会达到与DFT仿真相同的级别，但计算成本是几个数量级的事实允许应用该方法不仅可以应用于简单结构原型的预屏幕数据库，还可以应用于结构模型复杂无序或无定形p

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