预测阴离子导电膜聚合物材料的阳离子导电性和性稳定性:开发可解释的机器学习模型
Yin Kan Phua1, Tsuyohiko Fujigaya1,2,3, Koichiro Kato1,3,4
1Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.
Science and technology of advanced materials
|October 19, 2023
概括
机器学习模型现在可以高准确地预测离子交换膜 (AEM) 中的离子导电性. 这加速了燃料电池和电解剂的先进材料的开发,这对于可持续的经济至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学计算化学
背景情况:
- 阳离子交换膜 (AEM) 对于可持续的技术,如燃料电池和水电解剂至关重要.
- 目前的AEMs由于低离子导电性和耐久性而受到影响,阻碍了商业化.
- 开发新的AEM材料是资源密集的,机器学习 (ML) 的采用受到模型可解释性和复杂性的限制.
研究的目的:
- 开发第一个能够高准确地预测AEM离子导电性的ML模型,解决可解释性和聚合物表示性的局限性.
- 为了实现新型AEM材料的虚拟选,减少研发资源消耗.
- 通过揭示影响离子导电性的关键因素,揭示AEM的新设计原则.
主要方法:
- 开发新的机器学习模型,旨在实现高可解释性和对同聚合物和共聚合物的统一处理.
- 在AEM材料的各种数据集上对ML模型的培训和验证,包括新合成和退化状态.
- 使用根平均平方误差 (RMSE) 进行对离子导电性预测的模型性能评估.
主要成果:
- 在预测未见的AEM材料的离子导电性方面取得了高精度 (RMSE = 0.014 S cm-1).
- 模型在新合成和降解的AEM样本中显示出一致的性能.
- 确定了人类可以理解的预测逻辑,揭示了影响阳离子导电性的新型因素.
结论:
- 开发的ML模型为加速AEM材料发现和设计提供了一个强大的工具.
- 这种方法大大降低了AEM研发的资源密度.
- 该方法广泛适用于设计和发现AEM以外的各种聚合物基材料.
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