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RESEARCH PAPER · ACS Applied Materials & Interfaces · 2021

Machine Learning and Structural Design to Optimize the Flame Retardancy of Polymer Nanocomposites with Graphene Oxide Hydrogen Bonded Zinc Hydroxystannate

Fengqing Chen, Jinhe Wang, Zhen Guo, Fang Ming Jiang, Runhai Ouyang, Peng Ding

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Machine Learning and Structural Design to Optimize the Flame Retardancy of Polymer Nanocomposites with Graphene Oxide Hydrogen Bonded Zinc HydroxystannateOriginalAn Interpretable Machine Learning Model to Guide the Design of Organic Phosphorus-Containing Flame Retardants for Improving the Limiting Oxygen Index of Epoxy Resins0Physically soft magnetic films and devices: fabrication, properties, printability, and applications7Machine learning to optimize nanocomposite materials for electromagnetic interference shielding80Recent Advances in Zinc Hydroxystannate-Based Flame Retardant Polymer Blends35Modeling of toughening effect in rigid particulate-filled polymer composites by artificial intelligence: a review4Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review62Microstructure Observation, Accumulated Equivalent Stress, and Creep Strain Simulation of SPD Disconnector Solder Joint Based on Isothermal Aging6An adaptive framework to accelerate optimization of high flame retardant composites using machine learning41Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins76Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review20Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning56Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach45Material genome approach-based design of multi-functional self-curing epoxy resin with intrinsic flame retardancy and ultra-high modulus25Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets23The preparation of ZIF-67 and its synergistic effect on fire performance of intumescent flame retardant polypropylene12Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization13Optimizing interlaminar toughening of carbon-based filler/polymer nanocomposites by machine learning10Accelerating the discovery of N-annulated perylene organic sensitizers via an interpretable machine learning model10Machine learning-enabled rational design of organic flame retardants for enhanced fire safety of epoxy resin composites28Machine learning for expediting next-generation of fire-retardant polymer composites43The preparation of layered double hydroxide wrapped with triazine-based organic framework to enhance the flame retardancy for polypropylene17Machine learning-assisted design and scalable fabrication of high-performance fire-safe polycarbonate for advanced applications32The influence of well-dispersed mesoporous hydroxystannate ferrate anchored magnesium hydroxide hybrids on enhancing interfacial compatibility and fire safety of EVA/EPDM composites13Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning24Machine learning to optimize nonlinear conductive performance of composites for self‐adaptive electromagnetic shielding6Miscanthus floridulus-based intumescent flame retardant by green self-assembly for fully biological EP composites with commendable flame retardancy, smoke suppression and mechanical properties19Study on properties and simulation application scenarios of flame retarded modified konjac glucomannan organic and inorganic composite aerogel9Optimization of Flame Retardant Polypropylene via Machine Learning2Enhanced corrosion protection of copper in saline environments using bio-nanocomposite coatings based on chitosan and chitosan Schiff base11Optimization of Fire Protection Systems Using Machine Learning : Application to Intumescent Systems0Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach4Modeling of Nanoclay-Containing Polymer Composites Fire Resistance Properties2State of the art in machine learning for the purpose of optimizing and predicting the properties of polymeric nanocomposites2Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86Breaking through the trade-off between saturation magnetization and coercivity: A data-driven strategy14Machine learning-driven molecular generation for accelerated screening of high-performance flame retardants in epoxy resin composites13A machine learning platform for polymer flammability prediction8Recent Progress of Artificial Intelligence Application in Polymer Materials54Machine learning in polymer science: A new lens for physical and chemical exploration35Ant colony algorithm guided synthesis of flame retardants for enhanced polylactic acid flame retardancy3Enhancing Flame Retardancy in Polypropylene Composites: A Bayesian Optimization Approach0Machine learning-guided optimization of flame retardancy in rigid polyurethane foams0AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications10Reaction-to-fire characterization data driving the AI-assisted design of flame-retardant polymeric materials1A Novel Image Analysis Methodology for Data‐Driven Assessment of Polymer Combustion Dynamics in the Vertical Flame Test0Multifunctional engineering melamine-formaldehyde resin modified via in situ copolymerization of amino grafted polyborosiloxane: Enhanced fire resistance, flexibility, and surface robustness0Sparse data inverse design framework for flame-retardant polymer composites0

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  2. Physically soft magnetic films and devices: fabrication, properties, printability, and applications

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  3. Machine learning to optimize nanocomposite materials for electromagnetic interference shielding

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  4. Recent Advances in Zinc Hydroxystannate-Based Flame Retardant Polymer Blends

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  5. Modeling of toughening effect in rigid particulate-filled polymer composites by artificial intelligence: a review

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  6. Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review

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    Nagababu Andraju, Greg W. Curtzwiler, Yun Ji, Evguenii I. Kozliak, Prakash Ranganathan

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  7. Microstructure Observation, Accumulated Equivalent Stress, and Creep Strain Simulation of SPD Disconnector Solder Joint Based on Isothermal Aging

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  8. An adaptive framework to accelerate optimization of high flame retardant composites using machine learning

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  9. Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins

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  10. Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review

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  11. Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

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    I. S. N. V. R. Prasanth, Prabahar Jeevanandam, Periyasami Selvaraju, K. Sathish, S. K. Hasane Ahammad, P. Sujatha, M. Kaarthik, Mayakannan Selvaraju, Bashyam Sasikumar

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  12. Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach

    Composites Communications · 2023-04-16

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  13. Material genome approach-based design of multi-functional self-curing epoxy resin with intrinsic flame retardancy and ultra-high modulus

    Chemical Engineering Journal · 2023-06-15

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  14. Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets

    Applied Physics Letters · 2023-06-19

    Cheng Yan, Xiang Hua Lin, Xiaming Feng, Hongyu Yang, Patrick F. Mensah, Guoqiang Li

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  15. The preparation of ZIF-67 and its synergistic effect on fire performance of intumescent flame retardant polypropylene

    Journal of Thermal Analysis and Calorimetry · 2023-06-29

    Xuhui Yang, Siyu Jiang, Zhiyong Zhang, Bin Li, Miaojun Xu

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  16. Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization

    Computational Materials Science · 2023-09-03

    Fengqing Chen, Zhen Guo, Jinhe Wang, Runhai Ouyang, Dianpu Ma, Pei Gao, Fei Pan, Peng Ding

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  17. Optimizing interlaminar toughening of carbon-based filler/polymer nanocomposites by machine learning

    Polymer Testing · 2023-09-22

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  18. Accelerating the discovery of N-annulated perylene organic sensitizers via an interpretable machine learning model

    Journal of Molecular Structure · 2023-10-14

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  19. Machine learning-enabled rational design of organic flame retardants for enhanced fire safety of epoxy resin composites

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    Zhongwei Chen, Boran Yang, Nannan Song, Yufan Liu, Yufan Liu, Rong Feng, Xida Zhang, Tingting Chen, Qingwu Zhang, Juncheng Jiang, Tao Chen, Yuan Yu, Lian X. Liu

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  20. Machine learning for expediting next-generation of fire-retardant polymer composites

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  21. The preparation of layered double hydroxide wrapped with triazine-based organic framework to enhance the flame retardancy for polypropylene

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  23. The influence of well-dispersed mesoporous hydroxystannate ferrate anchored magnesium hydroxide hybrids on enhancing interfacial compatibility and fire safety of EVA/EPDM composites

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  31. Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach

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