Recognizing the best catalyst for a reaction

Lazaridou, Anna; Smith, Louise; Pattisson, Samuel; Dummer, Nicholas F.; Smit, Joost; Johnston, Peter; Hutchings, Graham J.

doi:10.1038/s41570-023-00470-5

Cited by 23 publications

(10 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the excellent reviews by Hutchings at al. (2015, 2017, 2023) [43][44][45] are mainly reviewing the historical development of Au-based catalysts. A more comprehensive review by Dai and coworkers (2015) [46] encompasses the general discussions on both metal and metal-free catalysts.…”

Section: Scope Of the Reviewmentioning

confidence: 99%

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Chen,

Wang,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

The growing demand of PVC calls for a more sustainable technology for its production. Nonetheless, acetylene hydrochlorination, an industrially relevant technology for the manufacture of vinyl chloride monomer of PVC, is hampered by the use of toxic and volatile mercury chloride catalysts that poses severe threads to human health and the environments. This stimulates numerous researches in the past decades on hunting alternative candidates to replace the mercury catalysts. Among the large numbers of catalyst fami-lies, distinct single-atom metal catalysts have been predicted or demonstrated as promising catalytic systems in the hydrochlorina-tion chemistry, thus stimulating a new wave in the broader explorations of this distinctive catalyst groups in the last five years. In this Review, we summarize the recent advances of single-atom catalysts in acetylene hydrochlorination with the focus on their syn-thesis methods, catalytic performance, and reaction mechanism. The catalyst categories include single metal atoms of Au, Pt, Pd, Ru, Cu, and Fe, and diatomic systems. Perspectives on the challenges and directions for future development regarding the innovation of synthetic strategy, recognition of performance descriptors, and developments of on-site characterization techniques are provided.

show abstract

Section: Scope Of the Reviewmentioning

confidence: 99%

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Chen,

Wang,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, these aspects have not been fully addressed in previous reviews. For instance, the excellent reviews by Hutchings at al. − mainly review the historical development of Au-based catalysts. A more comprehensive review by Dai and co-workers encompasses the general discussions on both metal and metal-free catalysts.…”

Section: Introductionmentioning

confidence: 99%

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Chen,

Wang,

Zhao

et al. 2024

ACS Catal.

View full text Add to dashboard Cite

The growing demand for polyvinyl chloride (PVC) calls for a cleaner technology for its production. Nonetheless, acetylene hydrochlorination, an industrially relevant technology for the manufacture of vinyl chloride monomer of PVC, is hampered by the use of toxic and volatile mercury chloride catalysts that pose severe threads to human health and the environments. This stimulated numerous research studies in the past decades on hunting alternative candidates to replace the mercury catalysts. Among the large numbers of catalyst families, distinct single-atom metal catalysts have been predicted or demonstrated as promising catalytic systems in the hydrochlorination chemistry, thus stimulating a wave in the broader explorations of this distinctive catalyst group in the last five years. In this Review, we summarize the recent advances of singleatom catalysts in acetylene hydrochlorination with the focus on their synthesis methods, catalytic performance, and reaction mechanism. The catalyst categories include single metal atoms of Au, Pt, Pd, Ru, Cu, and Fe and diatomic systems. Perspectives on the challenges and directions for future development regarding the innovation of synthetic strategies, recognition of performance descriptors, and developments of on-site characterization techniques are provided.

show abstract

“…However, not all tasks are optimally performed by ML, particularly in catalyst development, where high-quality data sets are scarce. These data sets, both in terms of quantity and quality, are crucial for effectively applying ML methodologies. , This scarcity of data sets leads to overfitting and decreased performance while training the model. In addition, predictions made using such models may lack generalizability and reproducibility.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Roh,

Oh,

Lee

et al. 2024

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R 2 of 0.942 at 2478 epochs, whereas the ANN achieved an R 2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models.

show abstract

Recognizing the best catalyst for a reaction

Cited by 23 publications

References 64 publications

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Advances in Single-Atom-Catalyzed Acetylene Hydrochlorination

Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane

Contact Info

Product

Resources

About