Many of the machine learning-based approaches for materials property predictions use low-cost computational data. The motivation for machine learning models is based on the orders of magnitude speedup compared to DFT calculations or experimental characterization. High-quality experimental materials data would be ideal for training these models; unfortunately, experimental data are typically costly to obtain. As a result, experimental databases are often smaller and less cohesive. Using band gap, we demonstrate how an ensemble learning approach allows us to efficiently model experimental data by combining models trained on otherwise disparate computational and experimental data. This approach demonstrates how disparate data sources can be incorporated into the modeling of sparsely represented experimental data. In the case of band gap prediction, we reduce the root mean squared error by over 9%.

用于材料属性预测的许多基于机器学习的方法都使用低成本的计算数据。机器学习模型的动机基于与DFT计算或实验表征相比数量级加速。高质量的实验材料数据对于训练这些模型将是理想的；不幸的是，获得实验数据通常是昂贵的。结果，实验数据库通常更小且缺乏凝聚力。使用带隙，我们演示了集成学习方法如何使我们能够通过组合在其他方面完全不同的计算和实验数据上训练的模型来有效地对实验数据进行建模。该方法演示了如何将完全不同的数据源合并到稀疏表示的实验数据的建模中。在带隙预测的情况下，