Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy,Journal of Materials Chemistry A

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Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy
Journal of Materials Chemistry A ( IF 10.7 ) Pub Date : 2020-10-13 , DOI: 10.1039/d0ta06583h
Xiaoxu Wang _{1,

2,

3,

4,

5} , Changxin Wang _{1,

2,

3,

4,

6} , Shinan Ci _{4,

7,

8,

9} , Yuan Ma _{1,

2,

3,

4,

6} , Tong Liu _{4,

10,

11,

12,

13} , Lei Gao _{1,

2,

3,

4,

6} , Ping Qian _{1,

2,

3,

4,

5} , Chunlin Ji _{4,

14,

15} , Yanjing Su _{1,

2,

3,

4,

6}

Affiliation

Beijing Advanced Innovation Center for Materials Genome Engineering
University of Science and Technology Beijing
Beijing 100083
China
Department of Physics
Corrosion and Protection Center
Science and Technology on Advanced High Temperature Structural Materials Laboratory
Beijing Institute of Aeronautical Materials
Beijing 100095
Beijing Key Laboratory of Cloud Computing Key Technology and Application
Beijing Computing Center
Beijing Academy of Science and Technology
Beijing 100094
Kuang-Chi Institute of Advanced Technology
Shenzhen

2D MXene materials have the advantages of versatile chemical composition, tunable layer thickness, and facile functionalization nature, and can be used as catalysts for the hydrogen evolution reaction (HER). However, tuning the thermal stability and activation of in-plane activity remain a challenge. We apply high-throughput density functional theory (DFT) calculations, together with a machine learning framework, to identify 2D MXene ordered binary alloy (OBA) activity trends and guide HER catalyst design. 2D MXenes of M_n+1X_nO₂ (n = 1, 2, 3; X = C, N) and OBA HER catalysts of M₂M′X₂O₂ and M₂M′₂X₃O₂ with 3d, 4d and 5d transition metal electrons were enumerated by screening, followed by catalytic activity, thermal stability, and conductivity computations. Our results indicate that 110 kinds of experimentally unexplored 2D MXene OBAs with thermostability and outstanding HER activity surpassing that of noble metal platinum were selected. Especially, the titanium element is mainly contained in the ideal catalysts of 2D MXene OBAs, which is consistent with the MXenes synthesized by experiments. Further, we show that descriptors developed using the AdaBoost ensemble learning model could accurately predict and uncover the essential geometric and chemical origin of HER activity, which is very consistent with the electronic insights. The advanced research strategy, which combines high-throughput computing with machine learning, shows robust ability for evaluating the activity trends and designing new complicated catalysts.

中文翻译：

通过计算机驱动的工作流程和整体学习策略，加快2D MXene催化剂发现氢气的反应

2D MXene材料具有通用的化学组成，可调节的层厚度和容易的官能化性质的优点，并且可用作氢气析出反应（HER）的催化剂。但是，调节热稳定性和激活面内活动仍然是一个挑战。我们应用高通量密度泛函理论（DFT）计算以及机器学习框架来确定2D MXene有序二元合金（OBA）活性趋势并指导HER催化剂设计。M的2D MXenes _{Ñ 1} X _Ñ ø ₂（Ñ = 1，2，3; X = C，N）和OBA HER催化剂M的₂ M'X ₂ ö ₂和M ₂ M' ₂X ₃ O ₂通过筛选列举出3d，4d和5d过渡金属电子，然后进行催化活性，热稳定性和电导率计算。我们的结果表明，选择了110种未经实验的2D MXene OBA，它们具有超过贵金属铂的热稳定性和出色的HER活性。特别是，钛元素主要包含在2D MXene OBA的理想催化剂中，这与通过实验合成的MXene一致。此外，我们表明，使用AdaBoost集成学习模型开发的描述符可以准确预测和揭示HER活动的基本几何和化学起源，这与电子见解非常一致。将高通量计算与机器学习相结合的高级研究策略，

更新日期：2020-11-04

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