大规模的玻璃过渡温度预测与一个等价神经网络用于选聚合物
Zheng Long1, Hongmei Lu1, Zhimin Zhang1
1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR, China.
ACS omega
|February 12, 2024
概括
一种新的聚合物信息学方法,Lieconv-Tg,可以准确地从聚合物结构中预测玻璃过渡温度 (Tg). 这种方法通过改进虚拟选和过更可靠的聚合物候选物来增强材料的发现.
科学领域:
- 聚合物科学 聚合物科学
- 材料 信息学 信息学
- 计算化学计算化学
背景情况:
- 聚合物的广化学空间为发现具有理想性质的新材料带来了挑战.
- 聚合物信息学通过属性预测和虚拟选加速材料设计.
研究的目的:
- 开发一种新的方法,Lieconv-Tg,用于从重复单位中预测聚合物玻璃过渡温度 (Tg).
- 提高聚合物材料发现和选的准确性和效率.
主要方法:
- 开发了Lieconv-Tg,这是一种使用3D聚合物结构进行Tg预测的等价深度学习模型.
- 从PolyInfo编制了7166个同聚合物的数据集 (数据集_Tg) 来进行模型培训.
- 实施过方法 (摩根指纹在聚合点 - MF@PP) 来完善选结果.
主要成果:
- 利康维-Tg比ECC表现优越,平均绝对误差 (MAE) 降低了约6个 (从约30到约24).
- 在验证和测试集中,R2值达到0.90,实现了高预测准确度.
- 通过使用开发的方法和过策略,从大型数据库 (PI1M) 中成功选出有前途的聚合物候选物.
结论:
- 利康V-Tg为预测聚合物Tg提供了强大而准确的方法,促进了合理的材料设计.
- 结合Lieconv-Tg和MF@PP过,为识别新型聚合物材料提供了更可靠的策略.
- 这项工作为加速发现具有定制性质的先进聚合物铺平了道路.
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