Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow

Machine learning (ML) methods can train a model to predict
material
properties by exploiting patterns in materials databases that arise
from structure–property relationships. However, the importance
of ML-based feature analysis and selection is often neglected when
creating such models. Such analysis and selection are especially important
when dealing with multifidelity data because they afford a complex
feature space. This work shows how a gradient-boosted statistical
feature-selection workflow can be used to train predictive models
that classify materials by their metallicity and predict their band
gap against experimental measurements, as well as computational data
that are derived from electronic-structure calculations. These models
are fine-tuned via Bayesian optimization, using solely the features
that are derived from chemical compositions of the materials data.
We test these models against experimental, computational, and a combination
of experimental and computational data. We find that the multifidelity
modeling option can reduce the number of features required to train
a model. The performance of our workflow is benchmarked against state-of-the-art
algorithms, the results of which demonstrate that our approach is
either comparable to or superior to them. The classification model
realized an accuracy score of 0.943, a macro-averaged F1-score of
0.940, area under the curve of the receiver operating characteristic
curve of 0.985, and an average precision of 0.977, while the regression
model achieved a mean absolute error of 0.246, a root-mean squared
error of 0.402, and R2 of 0.937. This
illustrates the efficacy of our modeling approach and highlights the
importance of thorough feature analysis and judicious selection over
a “black-box” approach to feature engineering in ML-based
modeling.

机器学习（ML）方法能够通过利用材料数据库中由结构-性能关系产生的模式来训练模型以预测材料性能。然而，在构建此类模型时，基于机器学习的特征分析和选择的必要性往往被忽视。在处理多保真度数据时，这种分析和选择尤为重要，因为它们提供了一个复杂的特征空间。本研究展示了如何利用梯度提升统计特征选择工作流程来训练预测模型，这些模型通过金属丰度对材料进行分类，并预测其带隙与实验测量值以及从电子结构计算中得到的计算数据。这些模型通过贝叶斯优化进行微调，仅使用从材料数据的化学组成中提取的特征。我们对这些模型进行了实验、计算以及实验与计算数据相结合的测试。我们发现，多保真度建模选项可以减少训练模型所需的特征数量。我们的工作流程的性能与最先进的算法进行了基准测试，结果显示，我们的方法要么与它们相当，要么优于它们。分类模型实现了0.943的准确率、0.940的宏平均F1分数、0.985的接收器操作特性曲线下面积以及0.977的平均精度，而回归模型实现了0.246的均方绝对误差、0.402的均方根误差和0.937的决定系数。这说明了我们建模方法的有效性，并突出了在基于机器学习的建模中对特征进行彻底分析和审慎选择的必要性，而非采用“黑盒”方法进行特征工程。