论文标题
材料属性预测的机器学习方法：示例聚合物兼容性
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
论文作者
论文摘要
材料特性的预测是一个关键问题，因为它对材料设计和筛选具有重要意义。我们提出了一种用于材料财产预测的全新和一般的机器学习方法。作为一个代表性的例子，选择聚合物兼容性以证明我们方法的有效性。具体而言，我们挖掘了来自相关文献的数据，以构建特定的数据库，并根据混合聚合物的基本分子结构和辅助组合组成进行预测。我们的模型在包含数千个条目的数据集上至少获得了75％的精度。我们证明可以通过机器学习方法来学习和模拟结构和属性之间的关系。
Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer compatibility is chosen to demonstrate the effectiveness of our method. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by machine learning method.
