Department of Chemistry, University of Houston, Houston, Texas 77004, United States.
Texas Center for Superconductivity, University of Houston, Houston, Texas 77004, United States.
J Phys Chem Lett. 2021 Jul 29;12(29):6760-6766. doi: 10.1021/acs.jpclett.1c01845. Epub 2021 Jul 15.
Assessing the hardness of structural materials at elevated temperatures is experimentally and computationally challenging, yet crucial for their success. In this work, a machine-learning method was developed to determine a material's temperature-dependent hardness based on its chemical composition and crystal structure. A total of 593 Vickers hardness data collected at various temperatures were extracted from the literature and used to train an extreme gradient boosting (XGBoost) machine-learning model. Applying a combination of composition descriptors and smooth overlap of atomic positions (SOAP) structural descriptors to represent these materials resulted in outstanding accuracy ( = 0.91; MAE = 2.52 GPa). The model's intrinsic variance was also measured by using a bootstrap aggregating (bagging) method, and the subsequent predictions showed strong agreement with the experimental data. The capability of the trained model was finally verified by demonstrating the model's ability to discriminate polymorphs, separate the properties of similar compositions, and reproduce the high-temperature hardness of several classic structural materials.
评估高温下结构材料的硬度在实验和计算上都具有挑战性，但对于它们的成功至关重要。在这项工作中，开发了一种基于材料化学成分和晶体结构来确定材料温度相关硬度的机器学习方法。从文献中提取了总共 593 个在不同温度下收集的维氏硬度数据，并用于训练极端梯度提升 (XGBoost) 机器学习模型。应用组合的成分描述符和原子位置平滑重叠 (SOAP) 结构描述符来表示这些材料，得到了出色的准确性（ = 0.91；MAE = 2.52 GPa）。还使用自举聚合（bagging）方法测量了模型的固有方差，随后的预测与实验数据吻合良好。最后通过展示模型区分多晶型体、分离相似成分的性质以及再现几种经典结构材料的高温硬度的能力，验证了训练模型的能力。
