论文标题
半监督的生成方法在化学无序化合物中的点缺失形成：应用于铀木质混合氧化物
Semi-supervised generative approach to point-defect formation in chemically disordered compounds: application to uranium-plutonium mixed oxides
论文作者
论文摘要
如今，机器学习方法已成为材料科学领域的共同用途。例如，它们可以帮助优化新材料的理化特性，或有助于表征高度复杂的化合物。化学无序的固定溶液的建模引起了一个特别具有挑战性的问题，其中某些特性取决于化学物种在晶体晶格中的分布。这是铀谷氨酸混合氧化物核燃料的缺陷特性的情况。可能的配置数量是如此之高，以至于如果用直接采样处理问题，问题就会变得棘手。因此，我们提出了一种基于生成建模的机器学习方法，以优化对这个大型配置空间的探索。使用混合物密度网络采用概率，半监督的方法来估计（u，pu）O2中热缺陷的浓度。我们表明，根据文献中可用的其他方法，基于缺陷形成能量状态的密度的预测，这种方法在计算上的成本效益要高得多。
Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical compounds. An especially challenging problem arises in the modeling of chemically disordered solid solutions, for which some properties depend on the distribution of chemical species in the crystal lattice. This is the case of defect properties of uranium-plutonium mixed oxides nuclear fuels. The number of possible configurations is so high that the problem becomes intractable if treated with direct sampling. We thus propose a machine learning approach, based on generative modeling, to optimize the exploration of this large configuration space. A probabilistic, semi-supervised approach using Mixture Density Network is applied to estimate the concentration of thermal defects in (U, Pu)O2. We show that this method, based on the prediction of the density of states of formation energy of a defect, is computationally much more cost-efficient compared to other approaches available in the literature.
