Research graph
References from Pareto Optimization of a Cobalt Carbide Catalyst for Syngas Conversion: Balancing Activity and Selectivity via Interpretable Active Learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Advances in the Development of Novel Cobalt Fischer–Tropsch Catalysts for Synthesis of Long-Chain Hydrocarbons and Clean Fuels
10.1021/cr050972v · 2007 · External reference
The Fischer–Tropsch process: 1950–2000
10.1016/s0920-5861(01)00453-9 · 2002 · External reference
Development of Novel Catalysts for Fischer–Tropsch Synthesis: Tuning the Product Selectivity
10.1002/cctc.201000071 · 2010 · External reference
Fischer–Tropsch Catalysts for the Production of Hydrocarbon Fuels with High Selectivity
10.1002/cssc.201300797 · 2014 · External reference
Fischer–Tropsch cobalt activations: The role of water on catalyst reduction
10.1016/j.cattod.2024.114559 · 2024 · External reference
Cobalt–Nickel Nanoparticles Supported on Reducible Oxides as Fischer–Tropsch Catalysts
10.1021/acscatal.0c00777 · 2020 · External reference
Efficient and Stable Production of Long-Chain Hydrocarbons over Hydrophobic Carbon-Encapsulated TiO2-Supported Ru Catalyst in Fischer–Tropsch Synthesis
10.1021/acscatal.4c02979 · 2024 · External reference
Key Role of CO Coverage for Chain Growth in Co-Based Fischer–Tropsch Synthesis
10.1021/acscatal.3c04844 · 2024 · External reference
Insight into the Formation of Co@Co2C Catalysts for Direct Synthesis of Higher Alcohols and Olefins from Syngas
10.1021/acscatal.7b02403 · 2018 · External reference
In situ monitoring during the transition of cobalt carbide to metal state and its application as Fischer–Tropsch catalyst in slurry phase
10.1016/j.jcat.2013.06.029 · 2013 · External reference
Tuning cobalt carbide wettability environment for Fischer–Tropsch to olefins with high carbon efficiency
10.1016/s1872-2067(23)64410-9 · 2023 · External reference
Highly selective production of olefins from syngas with modified ASF distribution model
10.1016/j.apcata.2018.07.006 · 2018 · External reference
Fischer–Tropsch Synthesis to Olefins: Catalytic Performance and Structure Evolution of Co2C-Based Catalysts under a CO2 Environment
10.1021/acscatal.9b02513 · 2019 · External reference
Constructing Co2C and ZSM-5 interface for enhanced activity in Fischer–Tropsch synthesis to olefins
10.1016/j.cej.2025.160936 · 2025 · External reference
Formation Mechanism of the Co2C Nanoprisms Studied with the CoCe System in the Fischer–Tropsch to Olefin Reaction
10.1021/acscatal.0c04504 · 2021 · External reference
Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter
10.1021/acs.iecr.1c05079 · 2022 · External reference
Unveiling the Mechanisms of Catalytic CO2 Electroreduction through Machine Learning
10.1021/acs.iecr.3c02698 · 2023 · External reference
Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures
10.1021/acs.iecr.4c01708 · 2024 · External reference
Distilling Knowledge from Catalysis Literature with Long-Context Large Language Model Agents
10.1021/acscatal.5c06431 · 2025 · External reference
Digital materials ecosystem: from databases to AI agents for autonomous discovery
10.1039/d5sc09229a · 2026 · External reference
Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives
10.1016/j.jcat.2025.116162 · 2025 · External reference
Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models
10.1002/anie.202526150 · 2026 · External reference
Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects
10.1016/j.cej.2024.152757 · 2024 · External reference
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
10.1039/d5cs00146c · 2025 · External reference
Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts
10.1021/acs.iecr.3c03147 · 2023 · External reference
Improving catalysts and operating conditions using machine learning in Fischer–Tropsch synthesis of jet fuels (C8-C16)
10.1016/j.ceja.2024.100702 · 2025 · External reference
Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis
10.1016/j.aichem.2024.100062 · 2024 · External reference
In Situ Active Site for CO Activation in Fe-Catalyzed Fischer–Tropsch Synthesis from Machine Learning
10.1021/jacs.1c04624 · 2021 · External reference
Selective Production of Hydrocarbons (C5+) from CO2 Hydrogenation: Exploring Clusters and Machine Learning
10.1021/acs.iecr.5c04024 · 2026 · External reference
Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts
10.1021/acssuschemeng.5c02957 · 2025 · External reference
Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization
10.1016/j.mtcomm.2025.112484 · 2025 · External reference
Gradient boosting for extreme quantile regression
10.1007/s10687-023-00473-x · 2023 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · External reference
CatBoost: unbiased boosting with categorical features
2019 · External reference
Random Forests
10.1023/a:1010933404324 · 2001 · External reference
Application of an Optimized SVR Model of Machine Learning
10.14257/ijmue.2014.9.6.08 · 2014 · External reference
Scikit-learn: Machine Learning in Python
2011 · External reference
Dealing with the big data challenges in AI for thermoelectric materials
10.1007/s40843-023-2777-2 · 2024 · External reference
Unresolved reference
External reference
Greedy Function Approximation: A Gradient Boosting Machine
10.1214/aos/1013203451 · 2001 · External reference
From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery
10.1021/acs.jcim.3c01642 · 2023 · External reference
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
10.1021/accountsmr.1c00244 · 2022 · External reference
Multiobjective Optimization of a Light Olefin Production Process via CO2 Hydrogenation Considering Yield and Energy Efficiency
10.1021/acs.iecr.4c01010 · 2024 · External reference
Accelerated multi-objective alloy discovery through efficient bayesian methods: Application to the FCC high entropy alloy space
10.1016/j.actamat.2025.121173 · 2025 · External reference
Machine Learning-Assisted Multiobjective Optimization of the Catalyst Layer in a Proton Exchange Membrane Water Electrolyzer
10.1021/acs.iecr.5c01224 · 2025 · External reference
Digital catalysis platform as a gateway to big data and AI-powered innovations in catalysis
10.1016/j.checat.2026.101775 · 2026 · External reference
Unlocking prediction and optimal design of CO2 methanation catalysts via active learning-enhanced interpretable ensemble learning
10.1016/j.cej.2025.161154 · 2025 · External reference
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · 2013 · External reference
Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
2025 · External reference
Mechanism of the Mn Promoter via CoMn Spinel for Morphology Control: Formation of Co2C Nanoprisms for Fischer–Tropsch to Olefins Reaction
10.1021/acscatal.7b02144 · 2017 · External reference
Tuning the catalytic CO hydrogenation to straight- and long-chain aldehydes/alcohols and olefins/paraffins
10.1038/ncomms13058 · 2016 · External reference
Tuning the Fischer–Tropsch reaction over CoxMnyLa/AC catalysts toward alcohols: Effects of La promotion
10.1016/j.jcat.2018.02.008 · 2018 · External reference
Dynamics of Co/Co2C redox cycle and their catalytic consequences in Fischer–Tropsch synthesis on cobalt–manganese catalysts
10.1016/j.cej.2022.140577 · 2023 · External reference
A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II
10.1109/4235.996017 · 2002 · External reference
Hydroxy-induced cobalt oxides for syngas to light olefins
10.1038/s41586-026-10204-4 · 2026 · External reference
Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models
10.1002/anie.202526150 · ExternalCitation · doi-reference
Development of Novel Catalysts for Fischer–Tropsch Synthesis: Tuning the Product Selectivity
10.1002/cctc.201000071 · ExternalCitation · doi-reference
Fischer–Tropsch Catalysts for the Production of Hydrocarbon Fuels with High Selectivity
10.1002/cssc.201300797 · ExternalCitation · doi-reference
Gradient boosting for extreme quantile regression
10.1007/s10687-023-00473-x · ExternalCitation · doi-reference
Dealing with the big data challenges in AI for thermoelectric materials
10.1007/s40843-023-2777-2 · ExternalCitation · doi-reference
Accelerated multi-objective alloy discovery through efficient bayesian methods: Application to the FCC high entropy alloy space
10.1016/j.actamat.2025.121173 · ExternalCitation · doi-reference
Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis
10.1016/j.aichem.2024.100062 · ExternalCitation · doi-reference
Highly selective production of olefins from syngas with modified ASF distribution model
10.1016/j.apcata.2018.07.006 · ExternalCitation · doi-reference
Fischer–Tropsch cobalt activations: The role of water on catalyst reduction
10.1016/j.cattod.2024.114559 · ExternalCitation · doi-reference
Dynamics of Co/Co2C redox cycle and their catalytic consequences in Fischer–Tropsch synthesis on cobalt–manganese catalysts
10.1016/j.cej.2022.140577 · ExternalCitation · doi-reference
Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects
10.1016/j.cej.2024.152757 · ExternalCitation · doi-reference
Constructing Co2C and ZSM-5 interface for enhanced activity in Fischer–Tropsch synthesis to olefins
10.1016/j.cej.2025.160936 · ExternalCitation · doi-reference
Unlocking prediction and optimal design of CO2 methanation catalysts via active learning-enhanced interpretable ensemble learning
10.1016/j.cej.2025.161154 · ExternalCitation · doi-reference
Improving catalysts and operating conditions using machine learning in Fischer–Tropsch synthesis of jet fuels (C8-C16)
10.1016/j.ceja.2024.100702 · ExternalCitation · doi-reference
Digital catalysis platform as a gateway to big data and AI-powered innovations in catalysis
10.1016/j.checat.2026.101775 · ExternalCitation · doi-reference
In situ monitoring during the transition of cobalt carbide to metal state and its application as Fischer–Tropsch catalyst in slurry phase
10.1016/j.jcat.2013.06.029 · ExternalCitation · doi-reference
Tuning the Fischer–Tropsch reaction over CoxMnyLa/AC catalysts toward alcohols: Effects of La promotion
10.1016/j.jcat.2018.02.008 · ExternalCitation · doi-reference
Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives
10.1016/j.jcat.2025.116162 · ExternalCitation · doi-reference
Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization
10.1016/j.mtcomm.2025.112484 · ExternalCitation · doi-reference
The Fischer–Tropsch process: 1950–2000
10.1016/s0920-5861(01)00453-9 · ExternalCitation · doi-reference
Tuning cobalt carbide wettability environment for Fischer–Tropsch to olefins with high carbon efficiency
10.1016/s1872-2067(23)64410-9 · ExternalCitation · doi-reference
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
10.1021/accountsmr.1c00244 · ExternalCitation · doi-reference
Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter
10.1021/acs.iecr.1c05079 · ExternalCitation · doi-reference
Unveiling the Mechanisms of Catalytic CO2 Electroreduction through Machine Learning
10.1021/acs.iecr.3c02698 · ExternalCitation · doi-reference
Application of Machine Learning to Fischer–Tropsch Synthesis for Cobalt Catalysts
10.1021/acs.iecr.3c03147 · ExternalCitation · doi-reference
Multiobjective Optimization of a Light Olefin Production Process via CO2 Hydrogenation Considering Yield and Energy Efficiency
10.1021/acs.iecr.4c01010 · ExternalCitation · doi-reference
Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures
10.1021/acs.iecr.4c01708 · ExternalCitation · doi-reference
Machine Learning-Assisted Multiobjective Optimization of the Catalyst Layer in a Proton Exchange Membrane Water Electrolyzer
10.1021/acs.iecr.5c01224 · ExternalCitation · doi-reference
Selective Production of Hydrocarbons (C5+) from CO2 Hydrogenation: Exploring Clusters and Machine Learning
10.1021/acs.iecr.5c04024 · ExternalCitation · doi-reference
From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery
10.1021/acs.jcim.3c01642 · ExternalCitation · doi-reference
Cobalt–Nickel Nanoparticles Supported on Reducible Oxides as Fischer–Tropsch Catalysts
10.1021/acscatal.0c00777 · ExternalCitation · doi-reference
Formation Mechanism of the Co2C Nanoprisms Studied with the CoCe System in the Fischer–Tropsch to Olefin Reaction
10.1021/acscatal.0c04504 · ExternalCitation · doi-reference
Key Role of CO Coverage for Chain Growth in Co-Based Fischer–Tropsch Synthesis
10.1021/acscatal.3c04844 · ExternalCitation · doi-reference
Efficient and Stable Production of Long-Chain Hydrocarbons over Hydrophobic Carbon-Encapsulated TiO2-Supported Ru Catalyst in Fischer–Tropsch Synthesis
10.1021/acscatal.4c02979 · ExternalCitation · doi-reference
Distilling Knowledge from Catalysis Literature with Long-Context Large Language Model Agents
10.1021/acscatal.5c06431 · ExternalCitation · doi-reference
Mechanism of the Mn Promoter via CoMn Spinel for Morphology Control: Formation of Co2C Nanoprisms for Fischer–Tropsch to Olefins Reaction
10.1021/acscatal.7b02144 · ExternalCitation · doi-reference
Insight into the Formation of Co@Co2C Catalysts for Direct Synthesis of Higher Alcohols and Olefins from Syngas
10.1021/acscatal.7b02403 · ExternalCitation · doi-reference
Fischer–Tropsch Synthesis to Olefins: Catalytic Performance and Structure Evolution of Co2C-Based Catalysts under a CO2 Environment
10.1021/acscatal.9b02513 · ExternalCitation · doi-reference
Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts
10.1021/acssuschemeng.5c02957 · ExternalCitation · doi-reference
Advances in the Development of Novel Cobalt Fischer–Tropsch Catalysts for Synthesis of Long-Chain Hydrocarbons and Clean Fuels
10.1021/cr050972v · ExternalCitation · doi-reference
In Situ Active Site for CO Activation in Fe-Catalyzed Fischer–Tropsch Synthesis from Machine Learning
10.1021/jacs.1c04624 · ExternalCitation · doi-reference
Random Forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Tuning the catalytic CO hydrogenation to straight- and long-chain aldehydes/alcohols and olefins/paraffins
10.1038/ncomms13058 · ExternalCitation · doi-reference
Hydroxy-induced cobalt oxides for syngas to light olefins
10.1038/s41586-026-10204-4 · ExternalCitation · doi-reference
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
10.1039/d5cs00146c · ExternalCitation · doi-reference
Digital materials ecosystem: from databases to AI agents for autonomous discovery
10.1039/d5sc09229a · ExternalCitation · doi-reference
A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II
10.1109/4235.996017 · ExternalCitation · doi-reference
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
10.1111/j.1600-0587.2012.07348.x · ExternalCitation · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · ExternalCitation · doi-reference
Greedy Function Approximation: A Gradient Boosting Machine
10.1214/aos/1013203451 · ExternalCitation · doi-reference
Application of an Optimized SVR Model of Machine Learning
10.14257/ijmue.2014.9.6.08 · ExternalCitation · doi-reference
