In this work we use the random forest model to predict the glass-forming ability (GFA) of metallic alloys by leveraging a previously published study and dataset. The new model with optimized hyperparameters successfully boosts the prediction accuracy by over 12%. The improvement is primarily attributed to the additional critical features (e.g. mixing entropy and total electronegativity) that has been identified. Although the improved model is still far away from being satisfactory (R² = 0.64) due to the extremely unbalanced nature of the dataset, the new identified features will significantly facilitate the future model development with more and more emerging experimental data.

在这项工作中，我们利用随机森林模型通过利用先前发表的研究和数据集来预测金属合金的玻璃形成能力（GFA）。具有优化的超参数的新模型成功地将预测准确性提高了12％以上。改善主要归因于已确定的其他关键特征（例如，混合熵和总电负性）。尽管 由于数据集的极不平衡特性，改进后的模型仍远远不能令人满意（R ² = 0.64），但新识别的特征将通过越来越多的新兴实验数据极大地促进未来模型的开发。