Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells,Energy & Environmental Science

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Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells
Energy & Environmental Science ( IF 32.4 ) Pub Date : 2021-6-2 , DOI: 10.1039/d1ee01170g
Xiuyang Zou _{1,

2,

3,

4,

5} , Ji Pan _{1,

2,

3,

4,

5} , Zhe Sun _{1,

2,

3,

4,

5} , Bowen Wang _{1,

2,

3,

4,

5} , Zhiyu Jin _{1,

2,

3,

4,

5} , Guodong Xu _{1,

2,

3,

4,

5} , Feng Yan _{1,

2,

3,

4,

5}

Affiliation

The degradation of anion exchange membranes (AEMs) hindered the practical applications of alkaline membrane fuel cells. This issue has inspired a large number of both experimental and theoretical studies. However, it is highly difficult to draw universal laws from the resulting data. Here, for the first time, artificial intelligence (AI) technology was presented to forecast the chemical stability of AEMs for fuel cells. The chemical stability of AEMs was quantified by Hammett substituent constants based on a materials genomics strategy, and then classified by a decision tree. Among five machine learning algorithms applied, the artificial neural network (ANN) showed the highest accuracy in predicting the chemical stability of AEMs (R² = 0.9978). Combined with the computational works, long-term chemical stability experiments were conducted to demonstrate the robustness and prediction accuracy of the proposed approach. This study highlights the potential of data-driven modelling for predicting the alkaline stability of AEMs, and thus unnecessary experiments can be avoided for the development of alkaline membrane fuel cells.

中文翻译：

燃料电池碱性阴离子交换膜的机器学习分析与预测模型

阴离子交换膜（AEM）的降解阻碍了碱性膜燃料电池的实际应用。这个问题激发了大量的实验和理论研究。然而，从结果数据中得出普遍规律是非常困难的。在这里，首次提出了人工智能 (AI) 技术来预测燃料电池 AEM 的化学稳定性。AEMs 的化学稳定性通过基于材料基因组学策略的 Hammett 取代常数进行量化，然后通过决策树进行分类。在应用的五种机器学习算法中，人工神经网络 (ANN) 在预测 AEMs 的化学稳定性方面表现出最高的准确度 ( R ²= 0.9978）。结合计算工作，进行了长期化学稳定性实验，以证明所提出方法的稳健性和预测准确性。这项研究强调了数据驱动建模在预测 AEM 碱性稳定性方面的潜力，因此可以避免开发碱性膜燃料电池的不必要的实验。

更新日期：2021-06-08

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