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Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2021-10-21 , DOI: 10.1021/acsami.1c13236 Jingnan Zheng 1 , Xiang Sun , Jiaxi Hu , ShiBin Wang 1 , Zihao Yao 1 , Shengwei Deng 1 , Xiang Pan , Zhiyan Pan , Jianguo Wang 1
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2021-10-21 , DOI: 10.1021/acsami.1c13236 Jingnan Zheng 1 , Xiang Sun , Jiaxi Hu , ShiBin Wang 1 , Zihao Yao 1 , Shengwei Deng 1 , Xiang Pan , Zhiyan Pan , Jianguo Wang 1
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Two-dimensional (2D) materials have been developed into various catalysts with high performance, but employing them for developing highly stable and active nonprecious hydrogen evolution reaction (HER) catalysts still encounters many challenges. To this end, the machine learning (ML) screening of HER catalysts is accelerated by using genetic programming (GP) of symbolic transformers for various typical 2D MA2Z4 materials. The values of the Gibbs free energy of hydrogen adsorption (ΔGH*) are accurately and rapidly predicted via extreme gradient boosting regression by using only simple GP-processed elemental features, with a low predictive root-mean-square error of 0.14 eV. With the analysis of ML and density functional theory (DFT) methods, it is found that various electronic structural properties of metal atoms and the p-band center of surface atoms play a crucial role in regulating the HER performance. Based on these findings, NbSi2N4 and VSi2N4 are discovered to be active catalysts with thermodynamical and dynamical stability as ΔGH* approaches to zero (−0.041 and 0.024 eV). In addition, DFT calculations reveal that these catalysts also exhibit good deuterium evolution reaction (DER) performance. Overall, a multistep workflow is developed through ML models combined with DFT calculations for efficiently screening the potential HER and DER catalysts from 2D materials with the same crystal prototype, which is believed to have significant contribution to catalyst design and fabrication.
中文翻译：
符号变压器加速 MA2Z4 材料中氢和氘演化反应催化剂的机器学习筛选
二维 (2D) 材料已被开发成各种高性能催化剂，但将其用于开发高度稳定和活性的非贵重析氢反应 (HER) 催化剂仍面临许多挑战。为此，通过对各种典型的 2D MA 2 Z 4材料使用符号转换器的遗传编程 (GP)，加速了 HER 催化剂的机器学习 (ML) 筛选。氢吸附的吉布斯自由能 (Δ G H*) 仅使用简单的 GP 处理的元素特征，通过极端梯度提升回归准确快速地预测，预测均方根误差为 0.14 eV。通过对ML和密度泛函理论（DFT）方法的分析，发现金属原子的各种电子结构特性和表面原子的p带中心在调节HER性能方面起着至关重要的作用。基于这些发现，发现 NbSi 2 N 4和 VSi 2 N 4是具有热力学和动力学稳定性的活性催化剂，Δ G H*接近于零（-0.041 和 0.024 eV）。此外，DFT 计算表明，这些催化剂还表现出良好的析氢反应（DER）性能。总体而言，通过 ML 模型结合 DFT 计算开发了一个多步骤工作流程，用于从具有相同晶体原型的 2D 材料中有效筛选潜在的 HER 和 DER 催化剂，这被认为对催化剂设计和制造有重大贡献。
更新日期：2021-11-03
中文翻译：
符号变压器加速 MA2Z4 材料中氢和氘演化反应催化剂的机器学习筛选
二维 (2D) 材料已被开发成各种高性能催化剂，但将其用于开发高度稳定和活性的非贵重析氢反应 (HER) 催化剂仍面临许多挑战。为此，通过对各种典型的 2D MA 2 Z 4材料使用符号转换器的遗传编程 (GP)，加速了 HER 催化剂的机器学习 (ML) 筛选。氢吸附的吉布斯自由能 (Δ G H*) 仅使用简单的 GP 处理的元素特征，通过极端梯度提升回归准确快速地预测，预测均方根误差为 0.14 eV。通过对ML和密度泛函理论（DFT）方法的分析，发现金属原子的各种电子结构特性和表面原子的p带中心在调节HER性能方面起着至关重要的作用。基于这些发现，发现 NbSi 2 N 4和 VSi 2 N 4是具有热力学和动力学稳定性的活性催化剂，Δ G H*接近于零（-0.041 和 0.024 eV）。此外，DFT 计算表明，这些催化剂还表现出良好的析氢反应（DER）性能。总体而言，通过 ML 模型结合 DFT 计算开发了一个多步骤工作流程，用于从具有相同晶体原型的 2D 材料中有效筛选潜在的 HER 和 DER 催化剂，这被认为对催化剂设计和制造有重大贡献。
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