Atomistic simulations of H trapping at grain boundaries in ferritic alloys

铁素体合金晶界处 H 捕获的原子模拟

• 批准号: 535248809
• 负责人: Privatdozent Dr. Thomas Hammerschmidt
• 金额: --
• 依托单位: Abteilung Mikromechanische und makroskopische Modellierung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/535248809?language=en
• 关键词: Atomistic; simulations; trapping; grain; boundaries

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.

一个有效的氢战略，为气候中立的能源生产，需要一个基础设施的供应和需求连接的可用性。沿着氢的生产、储存和运输链，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（概括为术语氢脆）构成了相当大的挑战。大多数氢脆机制的共同起源是金属微观结构中H原子的高迁移率。因此，对氢技术的金属材料的优化的一个重要步骤是可靠的预测氢捕获和分布的微观结构。 在这个项目中，我们专注于了解铁和铁素体合金中H在晶界（GBs）处的溶解度。我们的目标是与H的偏好不同的偏析网站在GB，即偏析能量，局部原子环境中的H原子周围的局部几何形状，化学和应力/应变状态。为此，我们通过体心立方晶格中GB自由度空间的系统采样，构建了GB结构和能量的数据库。为了从这些数据中获得结构/属性关系，我们将开发一套分析工具，使用基于结构的模型，以结构单元模型的精神，以及更抽象的局部原子环境的数值表示。通过这种方式，我们希望一方面提供GB的直观表征方案，其允许定性预测GB性质，例如其迁移率和捕获势，另一方面通过机器学习方法提供用于定量预测GB能量和偏析能量的描述符，即H的广义化学势，以及一般晶界的广义化学势。