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RESEARCH PAPER · Journal of environmental chemical engineering · 2022

Explainable machine learning for carbon dioxide adsorption on porous carbon

Chen Xie, Yunchao Xie, Chi Zhang, Hongxing Dong, Long Zhang

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Explainable machine learning for carbon dioxide adsorption on porous carbonOriginalCO2 capture by various nanoparticles: Recent development and prospective65Machine learning-assisted selection of adsorption-based carbon dioxide capture materials39Machine learning and Shapley Additive Explanation-based interpretable prediction of the electrocatalytic performance of N-doped carbon materials64Explainable molecular simulation and machine learning for carbon dioxide adsorption on magnesium oxide29Abordagens de aprendizado de máquina para prever o equilíbrio de adsorção de gases leves em zeólitas, carvões ativados e redes metalorgânicas0Nanomaterials for CO2 Capture from Gas Streams9The adsorption and release mechanism of different aged microplastics toward Hg(II) via batch experiment and the deep learning method32Cutting-Edge Applications of Machine Learning in the Electrochemical Reduction of CO20Leveraging machine learning in porous media89Investigation of light gas adsorption by microporous materials using data analysis and decision tree machine learning algorithm1Machine learning and experimentally exploring the controversial role of nitrogen in CO2 uptake by waste-derived nitrogen-containing porous carbons15Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning57Adsorption capability and regenerability of carbon slit micropores for CO2 capture14Accelerated Discovery of CO 2 Solid Sorbents Using Active Machine Learning: Review and Perspectives18Prediction of CO2 adsorption of biochar under KOH activation via machine learning14Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality30Nanomaterials for Capturing and Storing CO₂: A Sequestration Approach2Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review16Prediction of perfluorooctanoic acid adsorption properties of porous carbon materials based on machine learning10Exploring machine learning models to predict atmospheric water harvesting with an ion deposition membrane10A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Decoding the CO2 adsorption of nitrogen-doped carbon under variable temperature and pressure conditions: A machine learning guideline7Machine learning-aided engineering of biochar from biomass pyrolysis and activation processes for enhanced CO2 adsorption8Prediction of CO2 adsorption performance of biochar based on machine learning1Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques13Machine learning assisted valorization of biochar: Effects of chemical activation on physicochemical properties3Adsorption isotherm prediction of KOH activated AC/CO2 pairs employing a fully connected neural network1Bridging classical and intelligent models: machine learning-guided adsorption isotherms for tailored acetone capture on porous carbon3eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing50Predictive modeling and feature attribution of CO₂ adsorption on LDH-derived materials using machine learning approach7Design of active sites for amine-functionalized direct air capture materials using integrated high-throughput calculations and machine learning8From data to catalysis: advances and prospects of machine learning-driven electrocatalytic CO 2 reduction3Synergizing advanced materials and artificial intelligence for next-generation carbon capture, utilization, and storage (CCUS): a review10Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives19High-capacity removal of crystal violet using ZIF-8/graphene quantum dot composite with RSM optimization and explainable machine learning6An interpretable ensemble machine learning model for predicting carbon dioxide adsorption on magnesium oxide-based sorbents23Enabling Direct Engineering of Porous Biochar with Machine Learning Using Only Simple Feedstock Information for Efficient Adsorption of CO 21Interpretable machine learning-based prediction and optimization of per- and polyfluoroalkyl substances chain length-dependent adsorption behavior on porous carbon materials with experimental validation0Comprehensive review of nitrogen‐doped porous carbon materials for CO 2 capture: synthesis techniques, computational modelling, machine learning and future perspectives3Machine learning approach for MOF-based photocatalytic CO2 reduction to fuels0Biogenic Synthesis of Stabilized α-Fe₂O₃ Nanoparticles Using Teucrium royleanum for Antimicrobial and Dye Degradation Applications0

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