An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction

Chen, An; Cai, Junfei; Wang, Zhilong; Han, Yanqiang; Ye, Simin; Li, Jinjin

doi:10.1016/j.jechem.2022.11.035

Cited by 11 publications

(5 citation statements)

References 47 publications

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“…In the case of large datasets, however, it may require a considerable amount of time and memory. Also, other algorithms, like logistic regression and decision trees, are utilized, which require strict mathematical reasoning, strong interpretation, and fast running speeds, making them suitable for small datasets [ 128 , 129 ].…”

Section: General Process Of Machine Learningmentioning

confidence: 99%

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Li,

Wu,

Zhang

et al. 2023

Nano-Micro Lett.

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Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

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Section: General Process Of Machine Learningmentioning

confidence: 99%

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Li,

Wu,

Zhang

et al. 2023

Nano-Micro Lett.

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“…adsorption energy, [207][208][209][210][211] and thermal conductivity, [212][213][214][215][216] and solar still performance. [217,218]…”

Section: Other Explorationsmentioning

confidence: 99%

“…It can be seen that with powerful data analysis capabilities and low research costs, AI has been widely used in property prediction, material structure search, and new material design. At the application level, AI not only has great advantages over traditional calculation methods in different fields, but also has more and more achievements in different material modeling tasks, such as electronic structure, [ 51,191–193 ] ionic conductivity, [ 83,94,194 ] stability, [ 195–198 ] mechanical property, [ 199–201 ] optical property, [ 202–204 ] magnetism, [ 205,206 ] adsorption energy, [ 207–211 ] and thermal conductivity, [ 212–216 ] and solar still performance. [ 217,218 ]…”

Section: Matgpt: Vane Of Materials Informaticsmentioning

confidence: 99%

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Wang,

Chen,

Tao

et al. 2023

Advanced Materials

Self Cite

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Combining materials science, artificial intelligence (AI), physical chemistry and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of “GPT AI” shows that the scientific research field has entered the era of intelligent civilization with “data” as the basic factor and “algorithm + computing power” as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads us to think more about materials informatics, both in terms of opportunities and challenges. In this review, we provide a comprehensive discussion of AI models, materials infrastructures, and review the current advances in the discovery and design of new materials. With the rise of new research paradigms triggered by “AI for Science”, we propose the vane of materials informatics: “MatGPT”, and carry out the technical path planning from the aspects of data, descriptors, generative models, pre‐training models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics. Finally, we discuss the challenges and constraints faced by materials informatics, in order to achieve a more digital, intelligent and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists. This article is protected by copyright. All rights reserved

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“…Machine learning tools can be very handy for efficient, rapid (descriptor-based) catalyst screening and design, 62 , 63 , 64 , 65 , 66 , 67 , 68 especially in discovering new catalytic compositions when the chemistry of the individual constituents is relatively simple such as metals. The compositions so obtained can then be modeled and subjected to detailed first-principles-based structure, active site elucidation, and characterization/validation, as explained in previous sections.…”

Section: Operando Catalyst Structure Modeling and Validationmentioning

confidence: 99%

“… 70 Similar studies exist on the discovery of atomically dispersed/single-atom catalysts for hydrogen evolution reaction applications. 63 , 64 Sun et al. 63 proposed a data-clustering algorithm called the fuzzy C-means model for data separation in ML and developed guidelines for the rapid catalyst screening and discovery of graphdiyne-based atomic catalysts.…”

Section: Operando Catalyst Structure Modeling and Validationmentioning

confidence: 99%

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Rajan¹,

Pushkar²,

Dharmalingam³

et al. 2023

iScience

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An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction

Cited by 11 publications

References 47 publications

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

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