Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis,ACS Applied Energy Materials

当前位置： X-MOL 学术 › ACS Appl. Energy Mater. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis
ACS Applied Energy Materials ( IF 5.4 ) Pub Date : 2020-08-20 , DOI: 10.1021/acsaem.0c01466
Mohammad Rezaul Karim ₁ , Magali Ferrandon ₂ , Samantha Medina ₂ , Elliot Sture ₂ , Nancy Kariuki ₂ , Deborah J. Myers ₂ , Edward F. Holby ₃ , Piotr Zelenay ₄ , Towfiq Ahmed ₁

Affiliation

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g_catalyst in the original data set. This combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

中文翻译：

结合高通量实验和回归算法优化无PGM的ORR电催化剂的合成

在过去的几十年中，作为用于氧还原反应（ORR）的基于Pt的电催化剂的替代品，无铂族金属（无PGM）材料的性能已取得显着改善。但是，ORR活性的进一步发展要求对前体和合成方法进行评估。为了应对这一挑战，我们使用高通量合成和活性测量方法生成了第一个包含36个样品的实验数据集。改变了几个控制参数（例如，Fe前驱物身份，前驱物含量和热解温度）。然后，我们根据所选的合成变量，开发了几种基于最新机器学习（ML）的回归模型来预测ORR活动。通过迭代算法，获得了更高的预测精度（较小的均方根误差）。我们发现，在几种方法中，梯度增强回归（GBR）和支持向量回归（SVR）最适合此数据集。在我们基于ML的替代模型的帮助下，我们决定更改催化剂的合成条件，与最大ORR质量活性值21.9 A / g相比，其测量的ORR活性提高了36％原始数据集中的_催化剂。这种结合了实验和机器学习方法的方法为开发高效的下一代ORR电催化剂以及更广泛的功能材料提供了一条有希望的道路。

更新日期：2020-09-28

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南