用于材料发现的可解释机器学习：预测潜在可形成的Nd-Fe-B晶体结构并提取结构-稳定性关系
Explainable Machine Learning for Materials Discovery: Predicting the Potentially Formable Nd-Fe-B Crystal Structures and Extracting Structure-Stability Relationship
摘要：
新的Nd-Fe-B晶体结构可以通过LATX主体结构的元素取代形成，包括镧系元素LA、过渡金属T和轻元素X作为B、C、N和O。然后将收集的5967个三元LATX材料样品用作主体结构。对于每个主体晶体结构，通过用Nd取代所有镧系元素位点、用Fe取代所有过渡金属位点、用B取代所有轻元素位点来产生取代晶体结构。高通量的第一性原理计算被应用于评估新创建的晶体结构的相稳定性，其中20个被发现是潜在的可成形的。基于监督和无监督学习技术的数据驱动方法被应用于估计新创建的NdFeB晶体结构的稳定性和分析结构稳定性关系。为了预测新创建的NdFeB结构的稳定性，三个监督学习模型，核岭回归，逻辑分类和决策树模型，从LATX主机晶体结构中学习;模型分别达到70.4%和68.7%的最大准确率和召回率。另一方面，我们提出的基于描述符相关性分析和高斯混合模型集成的无监督学习模型的准确率和召回率分别达到72.9%和82.1%，显着优于有监督模型。在捕捉和解释NdFeB晶体结构的结构稳定性关系时，无监督学习模型表明，平均原子配位数和Fe位的配位数是决定新取代NdFeB晶体结构相稳定性的最重要因素。
英文摘要：
New Nd-Fe-B crystal structures can be formed via the elemental substitution of LATX host structures, including lanthanides LA, transition metals T, and light elements X as B, C, N, and O. The 5967 samples of ternary LATX materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure stability relationship of the newly created NdFeB crystal structures. For predicting the stability for the newly created NdFeB structures, three supervised learning models, kernel ridge regression, logistic classification, and decision tree model, are learned from the LATX host crystal structures; the models achieve the maximum accuracy and recall scores of 70.4 and 68.7 percent, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieves accuracy and recall score of 72.9 and 82.1 percent, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure stability relationship of the NdFeB crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted NdFeB crystal structures.
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