Accelerating 2D MXene catalyst discovery for the hydrogen evolution reaction by computer-driven workflow and an ensemble learning strategy

2020 ◽  
Vol 8 (44) ◽  
pp. 23488-23497
Author(s):  
Xiaoxu Wang ◽  
Changxin Wang ◽  
Shinan Ci ◽  
Yuan Ma ◽  
Tong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrogen Evolution ◽  
Ensemble Learning ◽  
High Throughput ◽  
Hydrogen Evolution Reaction ◽  
Learning Strategy

Combining high-throughput calculation workflow with a machine learning strategy to accelerate 2D MXene HER catalyst discovery.

High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

2020 ◽  
Vol 124 (25) ◽  
pp. 13695-13705 ◽  
Author(s):  
Jingnan Zheng ◽  
Xiang Sun ◽  
Chenglong Qiu ◽  
Yilong Yan ◽  
Zihao Yao ◽  
...  
Keyword(s):  
Hydrogen Evolution ◽  
High Throughput ◽  
Hydrogen Evolution Reaction ◽  
High Throughput Screening

High-throughput theoretical optimization of the hydrogen evolution reaction on MXenes by transition metal modification

2018 ◽  
Vol 6 (10) ◽  
pp. 4271-4278 ◽  
Author(s):  
Pengkun Li ◽  
Jinguo Zhu ◽  
Albertus D. Handoko ◽  
Ruifeng Zhang ◽  
Haotian Wang ◽  
...  
Keyword(s):  
Energy Source ◽  
Transition Metal ◽  
Hydrogen Evolution ◽  
High Throughput ◽  
Hydrogen Evolution Reaction ◽  
Clean Energy ◽  
Clean Energy Source ◽  
Chemical Feedstock

Electrocatalysis has the potential to become a more sustainable approach to generate hydrogen as a clean energy source and chemical feedstock.

Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning

2020 ◽  
Vol 8 (11) ◽  
pp. 5663-5670 ◽  
Author(s):  
Shiru Lin ◽  
Haoxiang Xu ◽  
Yekun Wang ◽  
Xiao Cheng Zeng ◽  
Zhongfang Chen
Keyword(s):  
Machine Learning ◽  
Oxygen Reduction Reaction ◽  
Physical Properties ◽  
Oxygen Reduction ◽  
Hydrogen Evolution ◽  
Oxygen Evolution ◽  
Hydrogen Evolution Reaction ◽  
Oxygen Evolution Reaction ◽  
Reduction Reaction ◽  
Single Atom

The oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal–air batteries.

scholarly journals A Study of Two-Dimensional Single Atom-Supported MXenes as Hydrogen Evolution Reaction Catalysts Using DFT and Machine Learning

2021 ◽  
Author(s):  
HONGXING LIANG ◽  
Min Xu ◽  
Edouard Asselin
Keyword(s):  
Machine Learning ◽  
Thermal Stability ◽  
Hydrogen Evolution ◽  
Hydrogen Evolution Reaction ◽  
Machine Learning Algorithms ◽  
Single Atom ◽  
Design Stage ◽  
Two Dimensional ◽  
Effective Manner ◽  
Thermal Stability Of

<p></p><p>Dear Editor,</p> <p> </p> <p>Enclosed you will find the article entitled “A study of two-dimensional single atom-supported MXenes as hydrogen evolution reaction catalysts using DFT and machine learning” submitted for consideration to Journal of Materials Chemistry A. </p> <p> </p> <p>Existing studies predominantly focused on the hydrogen evolution reaction (HER) activities and stabilities of oxygen-terminated MXenes with single-atom loading. However, to the best of our knowledge, two-dimensional (2D) MXenes with different terminations (e.g. Br, I, Se, Te, B, Si, P, and NH) have not yet been investigated for the purposes of HER catalysis. Therefore, in this work, we considered the combined effect of the different surface terminations (B, NH, O, F, Si, P, S, Cl, Se, Br, Te, and I) and single atom loading (Ti, V, Fe, Co, Ni, Cu, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Hf, Ta, W, Re, Os, Ir, Pt, and Au) using DFT calculation. Gibbs free energy of hydrogen adsorption (reflecting activity) and the cohesive energy (a proxy for thermal stability) of these structures (264 in total) were calculated. We demonstrate that 21 uninvestigated 2D single-atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum and a thermal stability surpassing those of synthesized borophene sheet and MoS<sub>2</sub>. Moreover, all catalysts examined in this work were further randomly separated into training and test sets with a ratio of 7:3. The HER electrocatalytic performance and thermal stability of the catalysts in the test set were predicted by machine learning algorithms. Most importantly, we present a way to provide a comparable precision (root mean square error values for the activity and thermal stability predictions are 0.158 eV and 0.02 eV, respectively) to the published machine learning works by avoiding their adoption of complex electronic features and the associated high computational cost, and <i>by only using features that are </i><i>easily available in chemical repositories</i>. The algorithms used in this work are expected to help future researchers quickly screen single atom loaded MXenes HER catalysts at the initial design stage in a cost-effective manner. </p> <p> </p> <p>We have no financial interest in the subject or instrumentation used and there is no known conflict of interest. </p><br><p></p>

scholarly journals Machine-Learning Assisted Exploration: Toward the Next-Generation Catalyst for Hydrogen Evolution Reaction

2021 ◽  
Author(s):  
Sichen Wei ◽  
Soojung Baek ◽  
Hongyan Yue ◽  
Maomao Liu ◽  
Seok Joon Yun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electron Microscope ◽  
Hydrogen Evolution ◽  
Hydrogen Evolution Reaction ◽  
Electrochemical Characterizations ◽  
Bayesian Optimization ◽  
Material Structure ◽  
Hydrogen Energy ◽  
X Ray ◽  
Mos2 Catalyst

Abstract The development of active catalysts for hydrogen evolution reaction (HER) made from low-cost materials constitutes a crucial challenge in the utilization of hydrogen energy. Earth-abundant molybdenum disulfide (MoS2) has been discovered recently with good activity and stability for HER. In this report, we employ a hydrothermal technique for MoS2 synthesis which is a cost-effective and environmentally friendly approach and has the potential for future mass production. Machine-learning (ML) techniques are built and subsequently used within a Bayesian Optimization framework to validate the optimal parameter combinations for synthesizing high-quality MoS2 catalyst within the limited parameter space. Compared with the heavy-labor and time-consuming trial-and-error approach, the ML techniques provide a more efficient toolkit to assist exploration of the most effective HER catalyst in hydrothermal synthesis. To investigate the structure-property relationship, scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and various electrochemical characterizations have been conducted to investigate the superiority of the ML validated optimized sample. A strong correlation between the material structure and the HER performance has been observed for the optimized MoS2 catalyst.

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