Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells

Zou, Xiuyang; Pan, Ji Sheng; Sun, Zhe; Wang, Bowen; Jin, Zhiyu; Xu, Guodong; Yan, Feng

doi:10.1039/d1ee01170g

Cited by 41 publications

(27 citation statements)

References 63 publications

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“…[99a] In the future, MLs might be used to design more efficient, more stable and cost-in-effective electrocatalysts for alkaline fuel cells in addition to being as a powerful tool for anion-exchange membranes designs. [108] Direct methanol fuel cells (DMFCs) are among the most promising alternative energy technologies, [101a] owing to the high energy density of methanol and the non-toxicity of CO 2 and H 2 O. [100b,109] Platinum is the most effective catalyst for methanol oxidation reactions.…”

Section: Perspectivesmentioning

confidence: 99%

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Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Liu

Luo

Wang

et al. 2022

Adv Funct Materials

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(1 of 25)development, it is still far from meeting the increased demand. [3] The emergence of artificial intelligence (AI) has fundamentally changed the situation, which has significantly accelerated the discovery process, owing to greatly improved algorithms and developments in data science. [4] Machine learning (ML), a simple and practical AI framework based on computer and statistical science, is used to develop algorithms to learn from historic data without being explicitly programmed to obtain specific results. [5] It can investigate relationships that are hard to clearly and definitely model mathematically, providing insights for new scientific advancements related to highly complex with many uncertain twisted together factors. [6] There are usually three factors that govern the learning and prediction process of an ML: algorithms, data/database, and descriptors. [4] The algorithms involve data extraction, data filing, and propagation from mathematical derivation. [5,7] The data can be derived not only from experiments but also from theoretical calculations. [4] A number of databases based on experiments [8] and calculations [9] have already been established. The descriptors depend to a large extent on the predicted material or properties. Based on the algorithm, databases, and descriptors, the ML applications have been successfully implemented to support various energy materials with analysis tools (e.g., Python-based SciKit-Learn [10] and TensorFlow [11] ) in combination with workflow management tools (e.g., ASE [12] and Atomate [13] ). However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [14] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability. [5,15] Thus, suitable descriptors must be established to understand the structure-activity relationship. Although many efforts have been made to accelerate the rational design of homogeneous catalysts, [7b,16] heterogeneous catalysts, [7b,16b,17] and electrocatalysis, [3,18] the development of ML-assisted real catalysts is still in its infancy. Despite these considerable research efforts, the lack of universal selection tactics for descriptors bridging the gap between activity and structures impedes the application

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Section: Perspectivesmentioning

confidence: 99%

“…[ 99a ] In the future, MLs might be used to design more efficient, more stable and cost‐in‐effective electrocatalysts for alkaline fuel cells in addition to being as a powerful tool for anion‐exchange membranes designs. [ 108 ]…”

Section: Perspectivesmentioning

confidence: 99%

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Liu

Luo

Wang

et al. 2022

Adv Funct Materials

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“…Mainly focusing on the energy storage materials in DCs and LIBs, we have presented a short review of the applications of ML on the R&D process. It should be pointed out that ML has also been widely used in the R&D of other energy storage materials, including fuel cells, [196][197][198] thermoelectric materials, [199,200] supercapacitors, [201][202][203] and so on. So far, considerable progress has been made on the implementation of ML for discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization, which has tremendously accelerated the R&D pace of energy storage materials.…”

Section: Discussionmentioning

confidence: 99%

Machine learning in energy storage materials

Shen

Liu

Shen

et al. 2022

Interdisciplinary Materials

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With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium-ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science.

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“…In recent years, many researchers studied the development and applications of machine learning (ML) models in molecular design and performance improvement of energy materials and devices [ 154 , 155 , 156 , 157 , 158 , 159 ]. ML trained, based on QM/MM, may allow MD simulations at an accurate level close to the electronic-structure method chosen to generate a training set [ 160 , 161 ].…”

Section: All-atom Molecular Dynamics (Md) Simulationsmentioning

confidence: 99%

Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications

et al. 2022

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Anion Exchange Membrane (AEM) fuel cells have attracted growing interest, due to their encouraging advantages, including high power density and relatively low cost. AEM is a polymer matrix, which conducts hydroxide (OH−) ions, prevents physical contact of electrodes, and has positively charged head groups (mainly quaternary ammonium (QA) groups), covalently bound to the polymer backbone. The chemical instability of the quaternary ammonium (QA)-based head groups, at alkaline pH and elevated temperature, is a significant threshold in AEMFC technology. This review work aims to introduce recent studies on the chemical stability of various QA-based head groups and transportation of OH− ions in AEMFC, via modeling and simulation techniques, at different scales. It starts by introducing the fundamental theories behind AEM-based fuel-cell technology. In the main body of this review, we present selected computational studies that deal with the effects of various parameters on AEMs, via a variety of multi-length and multi-time-scale modeling and simulation methods. Such methods include electronic structure calculations via the quantum Density Functional Theory (DFT), ab initio, classical all-atom Molecular Dynamics (MD) simulations, and coarse-grained MD simulations. The explored processing and structural parameters include temperature, hydration levels, several QA-based head groups, various types of QA-based head groups and backbones, etc. Nowadays, many methods and software packages for molecular and materials modeling are available. Applications of such methods may help to understand the transportation mechanisms of OH− ions, the chemical stability of functional head groups, and many other relevant properties, leading to a performance-based molecular and structure design as well as, ultimately, improved AEM-based fuel cell performances. This contribution aims to introduce those molecular modeling methods and their recent applications to the AEM-based fuel cells research community.

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Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells

Abstract: 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,...

Cited by 41 publications

References 63 publications

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Machine learning in energy storage materials

Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications

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