Solute nanoclusters are critical to the structural and mechanical integrity of numerous alloys based on the b.c.c. Fe matrix, which have risen to prominence as candidates for advanced nuclear reactor applications. Because irradiation can profoundly alter the morphology and composition of these solute nanoclusters, it is critical to understand and predict solute clustering behavior in the presence of irradiation. In this study, we advance a simple theory to model irradiation-induced nanocluster evolution subject to different irradiating particles. The model is trained and validated with experimental data following an approach similar to training a machine learning algorithm, resulting in an agile model that can be used for rapid screening of new alloys. Using the model, nanocluster evolution is found to depend upon the disordering parameter (i.e., cluster morphology and dose rate) and irradiation temperature, and is most sensitive to the solute migration, vacancy formation, and vacancy migration energies. Results are discussed with respect to the irradiation temperature shift for varying irradiating particle types and dose rates.

中文翻译：

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体心立方铁基合金中辐照诱导溶质聚集的速率理论模型

溶质纳米团簇对于基于 bcc Fe 基体的众多合金的结构和机械完整性至关重要，这些合金已成为先进核反应堆应用的候选材料。由于辐照可以深刻改变这些溶质纳米团簇的形态和组成，因此了解和预测在辐照下的溶质团簇行为至关重要。在这项研究中，我们提出了一个简单的理论来模拟受不同辐照粒子影响的辐照诱导纳米团簇演化。该模型按照类似于训练机器学习算法的方法使用实验数据进行训练和验证，从而产生可用于快速筛选新合金的敏捷模型。使用该模型，发现纳米团簇演化取决于无序参数（即，簇形态和剂量率）和辐照温度，并且对溶质迁移、空位形成和空位迁移能量最敏感。讨论了不同辐照粒子类型和剂量率的辐照温度变化的结果。