Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

2021 ◽  
Author(s):  
Yuzhi Xu ◽  
Cheng-Wei Ju ◽  
Bo Li ◽  
Qiu-Shi Ma ◽  
Zhenyu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrogen Evolution ◽  
Conjugated Copolymers

scholarly journals Segment Descriptor Enabling Prediction of Electronic Properties and Photocatalytic Hydrogen Evolution Rate of Alternating Conjugated Copolymers Based on Machine Learning

2020 ◽  
Author(s):  
Yuzhi Xu ◽  
Cheng-Wei Ju ◽  
Bo Li ◽  
Qiu-Shi Ma ◽  
Lianjie Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Properties ◽  
Hydrogen Evolution ◽  
Molecular Structures ◽  
Evolution Rate ◽  
Test Accuracy ◽  
Photocatalytic Hydrogen Evolution ◽  
Alternating Copolymers ◽  
Hydrogen Evolution Rate ◽  
Conjugated Copolymers

Alternating conjugated copolymers have been regarded as promising candidates for photocatalytic hydrogen evolution due to the adjustability of their molecular structures and electronic properties. In this work, machine learning (ML) models with molecular fingerprint of segment descriptors (SD) have been successfully constructed to promote the accurate and universal prediction of electronic properties such as electron affinity, ionization potential and optical bandgap. Moreover, without any experimental values, a high-performance prediction classifier model toward photocatalytic hydrogen production of alternating copolymers has been developed with high accuracy (real-test accuracy = 0.91). Consequently, our results demonstrate accurate regression and classification models to disclose valuable influencing factors concerning hydrogen evolution rate (HER) of alternating copolymers.

Construction of Graphitic C3N4-Based Intramolecular Donor–Acceptor Conjugated Copolymers for Photocatalytic Hydrogen Evolution

ACS Catalysis ◽  
2015 ◽  
Vol 5 (9) ◽  
pp. 5008-5015 ◽  
Author(s):  
Xiangqian Fan ◽  
Lingxia Zhang ◽  
Ruolin Cheng ◽  
Min Wang ◽  
Mengli Li ◽  
...  
Keyword(s):  
Hydrogen Evolution ◽  
Photocatalytic Hydrogen Evolution ◽  
Donor Acceptor ◽  
Conjugated Copolymers

scholarly journals Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models

2020 ◽  
Author(s):  
Yuzhi Xu ◽  
Cheng-Wei Ju ◽  
Bo Li ◽  
Qiu-Shi Ma ◽  
Lianjie Zhang ◽  
...  
Keyword(s):  
Electronic Properties ◽  
Hydrogen Evolution ◽  
High Performance ◽  
Molecular Structures ◽  
Test Accuracy ◽  
Molecular Fingerprint ◽  
Photocatalytic Hydrogen Evolution ◽  
Alternating Copolymers ◽  
Universal Prediction ◽  
Conjugated Copolymers

Alternating conjugated copolymers have been regarded as promising candidates for photocatalytic hydrogen evolution due to the adjustability of their molecular structures and electronic properties. In this work, machine learning (ML) models with molecular fingerprint of segment descriptors (SD) have been successfully constructed to promote the accurate and universal prediction of electronic properties such as electron affinity, ionization potential and optical bandgap. Moreover, without any experimental values, a high-performance prediction classifier model toward photocatalytic hydrogen production of alternating copolymers has been developed with high accuracy (real-test accuracy = 0.91). Consequently, our results demonstrate accurate regression and classification models to disclose valuable influencing factors concerning hydrogen evolution rate (HER) of alternating copolymers.

Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials

2020 ◽  
Vol 526 ◽  
pp. 146522
Author(s):  
Xiang Sun ◽  
Jingnan Zheng ◽  
Yijing Gao ◽  
Chenglong Qiu ◽  
Yilong Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrogen Evolution

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.

Identifying Metallic Transition-Metal Dichalcogenides for Hydrogen Evolution through Multilevel High-Throughput Calculations and Machine Learning

2021 ◽  
Vol 12 (8) ◽  
pp. 2102-2111
Author(s):  
Nian Ran ◽  
Bo Sun ◽  
Wujie Qiu ◽  
Erhong Song ◽  
Tingwei Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Hydrogen Evolution ◽  
High Throughput ◽  
Transition Metal Dichalcogenides ◽  
Metal Dichalcogenides

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>

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