Atomistic simulations of H trapping at grain boundaries in ferritic alloys

一个有效的气候中性能源生产的氢气战略需要有一个连接供需的基础设施。在氢生产、储存和运输的链条上，氢原子与金属微结构的许多相互作用以及由此产生的退化现象(用氢脆一词来概括)构成了相当大的挑战。大多数氢脆机制的共同起源是金属组织中氢原子的高迁移率。因此，氢技术金属材料优化的重要一步是可靠地预测氢在组织中的捕获和分布。在这个项目中，我们重点了解氢在铁和铁素体合金中的晶界(GBs)的溶解度。我们的目标是根据局域几何、化学和应力/应变状态，将H在GBS不同偏析位置的偏好，即偏析能，与H原子周围的局域原子环境联系起来。为此，我们通过对体心立方晶格中GB自由度空间的系统采样，构建了GB结构和能量数据库。为了从这些数据中推导出结构/属性关系，我们将开发一组分析工具，使用基于结构的模型(本着结构单元模型的精神)，以及本地原子环境的更抽象的数字表示。这样，我们一方面希望提供一种直观的GB表征方案，它允许定性地预测GB的性质，例如它们的迁移率和陷阱势，另一方面允许通过机器学习方法定量预测GB的能量和偏析能量，即H的广义化学势，也就是一般的晶界。

An effective hydrogen strategy for a climate-neutral energy production requires the availability of an infrastructure for connecting supply and demand. Along the chain of H production, storage and transport, the many interactions of H atoms with metallic microstructures and the resulting degradation phenomena, summarized by the term hydrogen embrittlement, are posing a considerable challenge. Common origin of most hydrogen embrittlement mechanisms is the high mobility of H atoms in metallic microstructures. Hence, a major step towards an optimization of metallic materials for hydrogen technology is the reliable prediction of H trapping and distribution in the microstructure. In this project, we focus on understanding the solubility of H at grain boundaries (GBs) in iron and ferritic alloys. Our objective is to relate the preference of H for different segregation sites at GBs, i.e. the segregation energy, to the local atomic environment around the H atom in terms of local geometry, chemistry and stress/strain state. To this end, we construct a database of GB structures and energies via a systematic sampling of the space of the GB degrees of freedom in body centered cubic crystal lattices. To derive structure/property relationships from this data, we will develop a set of analysis tools using structure based models in the spirit of a structural unit model, as well as more abstract numerical representations of the local atomic environment. This way, we want to provide on the one hand an intuitive characterization scheme of GBs, which allows a qualitative prediction of GB properties, such as their mobility and trapping potential, and on the other hand the descriptors for a quantitative prediction of GB energies and segregation energies, i.e. the generalized chemical potential of H, also of general grain boundaries, via a machine learning approach.