A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility

• URL: http://arxiv.org/abs/2202.13554v1
• Date: Mon, 28 Feb 2022 05:48:05 GMT
• Title: A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
• Authors: Zhilong Liang, Zhiwei Li, Shuo Zhou, Yiwen Sun, Changshui Zhang, Jinying Yuan
• Abstract summary: 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.
• Score: 39.364776649251944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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