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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane
Jiwon Roh, Hyundo Park, Hyukwon Kwon, et al.
Applied Catalysis B Environment and Energy (2023) Vol. 343, pp. 123454-123454
Open Access | Times Cited: 24

Showing 24 citing articles:

Embracing data science in catalysis research
Manu Suvarna, Javier Pérez‐Ramírez
Nature Catalysis (2024) Vol. 7, Iss. 6, pp. 624-635
Closed Access | Times Cited: 27
Recent Advances in Coke Management for Dry Reforming of Methane over Ni-Based Catalysts
Zhen Xu, Eun Duck Park
Catalysts (2024) Vol. 14, Iss. 3, pp. 176-176
Open Access | Times Cited: 17
Catalysis in the digital age: Unlocking the power of data with machine learning
B. Moses Abraham, M. V. Jyothirmai, Priyanka Sinha, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 5
Open Access | Times Cited: 9
Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: beyond algorithm development
Mohd Nur Ikhmal Salehmin, Tiong Sieh Kiong, Hassan Mohamed, et al.
Journal of Energy Chemistry (2024) Vol. 99, pp. 223-252
Closed Access | Times Cited: 6
Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane
Jiwon Roh, Seunghyeon Oh, Donggyun Lee, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 10, pp. 4121-4131
Closed Access | Times Cited: 4
Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures
Qingchun Yang, Runjie Bao, Dongwen Rong, et al.
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 33, pp. 14727-14747
Closed Access | Times Cited: 4
Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation
Seunghyeon Oh, Jiwon Roh, Hyundo Park, et al.
ACS Sustainable Chemistry & Engineering (2025)
Closed Access
Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics
Leandro Goulart de Araujo, Léa Vilcocq, Pascal Fongarland, et al.
Chemical Engineering Journal (2025), pp. 160872-160872
Closed Access
Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development
Hao Wu, Mingxuan Chen, Hao Cheng, et al.
Journal of Materials Informatics (2025) Vol. 5, Iss. 2
Open Access
Studying the regeneration cycles of nickel-containing catalysts during high temperature gasification and prediction model fitting
V. Zsinka, Bálint Levente Tarcsay, Norbert Miskolczi
Biomass and Bioenergy (2025) Vol. 197, pp. 107745-107745
Open Access
Machine learning and experimental study on the activity decrease of VW/Ti for SCR at ultra-high temperature: the influence mechanism and regulation strategy
Jia Guo, Yongqi Zhao, Junjie Jiang, et al.
Process Safety and Environmental Protection (2025), pp. 107021-107021
Closed Access
Introducing an improved rime algorithm combined with gate current unit as an innovative stability monitoring and controlling model for flexible biogas-to-hydrogen/methanol system
Tao Tan, Zetao Huang, Zuhao Li, et al.
Renewable Energy (2025), pp. 123032-123032
Closed Access
Machine learning reveals structure-performance relationships of dry reforming of methane catalysts and the potential influencing mechanisms
Zhenyu Lin, Yuhan Cui, Yunjie Wang, et al.
International Journal of Hydrogen Energy (2025) Vol. 122, pp. 332-347
Closed Access
Redefining the Stability of Water Oxidation Electrocatalysts: Insights from Materials Databases and Machine Learning
Raúl A. Márquez, Erin Elizabeth Oefelein, Thuy Vy Le, et al.
ACS Materials Letters (2024) Vol. 6, Iss. 7, pp. 2905-2918
Closed Access | Times Cited: 2
Toward accelerated discovery of solid catalysts using extrapolative machine learning approach
Takashi Toyao
Chemistry Letters (2024) Vol. 53, Iss. 8
Open Access | Times Cited: 1
Accelerated Design of Nickel-Cobalt Based Catalysts for CO2 Hydrogenation with Human-in-the-Loop Active Machine Learning
Yasemen Kuddusi, Maarten R. Dobbelaere, Kevin M. Van Geem, et al.
Catalysis Science & Technology (2024)
Open Access | Times Cited: 1
Optimisation led energy-efficient arsenite and arsenate adsorption on various materials with machine learning
Jinsheng Huang, Waqar Muhammad Ashraf, Talha Ansar, et al.
Water Research (2024) Vol. 271, pp. 122815-122815
Closed Access | Times Cited: 1
Machine Learning-Based Adaptive Regression to Identify Nonlinear Dynamics of Biochemical Systems: A Case Study on Bio 2,3-Butanediol Distillation Process
Yeongryeol Choi, Bhavana Bhadriraju, Hyukwon Kwon, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 29, pp. 10869-10879
Closed Access
Catalyst development for the tri‐reforming of methane (TRM) process by integrated singular machine learning models
Paulo A.L. de Souza, Raja Muhammad Afzal, Felipe Gomes Camacho, et al.
The Canadian Journal of Chemical Engineering (2024)
Closed Access
Data-driven analysis in the selective oligomerization of long-chain linear alpha olefin on zeolite catalysts: A machine learning-based parameter study
Sung Woo Lee, Marcel Jonathan Hidajat, Seung Hyeok, et al.
Fuel Processing Technology (2024) Vol. 267, pp. 108164-108164
Closed Access
Novel inverse predictive system integrated with industrial lubricant information
Minseong Kim, Hyungtae Cho, Jongkoo Lim, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 142, pp. 109853-109853
Closed Access
Ensemble Model Approach for Predicting the Yield of Dehydrogenation Products during the Oxidative Dehydrogenation of n-Butane
Gazali Tanimu, Nurudeen A. Adegoke, Jimoh Olawale Ajadi, et al.
ACS Omega (2024) Vol. 10, Iss. 1, pp. 1198-1206
Open Access
Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning
Wentao Du, Patchanee Chammingkwan, Keisuke Takahashi, et al.
Journal of Catalysis (2024), pp. 115930-115930
Closed Access
Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane
Hyundo Park, Jiwon Roh, Hyungtae Cho, et al.
Journal of Materials Chemistry A (2023) Vol. 12, Iss. 3, pp. 1629-1641
Closed Access | Times Cited: 1
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