Accelerating the design of flame-retardant polypropylene composites: An approach driven by explainable ensemble machine learning and Bayesian optimization
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OpenAlex topics: Flame retardant materials and properties · Fire dynamics and safety research · Polymer crystallization and properties
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References (58)
Show source-provided references
1. Advanced flame-retardant methods for polymeric materials
2. Fire-resistant and flame-retardant surface finishing of polymers and textiles: a state-of-the-art review
3. Biopolymer-based flame retardants and flame-retardant materials
4. Different applications of machine learning approaches in materials science and engineering: comprehensive review10.1016/j.engappai.2024.108783 ↗
5. Application of machine learning for advanced material prediction and design10.1002/eom2.12194 ↗
6. Innovative materials science via machine learning
7. Accelerating materials discovery using machine learning10.1016/j.jmst.2020.12.010 ↗
8. Machine learning in materials science10.1002/inf2.12028 ↗
9. Early prediction of battery lifetime via a machine learning based framework10.1016/j.energy.2021.120205 ↗
10. Machine learning screening of metal-ion battery electrode materials
11. Performance analysis of Na-ion batteries by machine learning10.1016/j.jpowsour.2022.232126 ↗
12. Application-oriented design of machine learning paradigms for battery science10.1038/s41524-025-01575-9 ↗
13. Temperature excavation to boost machine learning battery thermochemical predictions10.1016/j.joule.2024.07.002 ↗
14. Accelerated design of electrodes for liquid metal battery by machine learning10.1016/j.ensm.2022.12.047 ↗
15. Unveiling future superconductors through machine learning
16. Machine-learning accelerated prediction of two-dimensional conventional superconductors10.1039/D4MH01753F ↗
17. High- Tc superconductor candidates proposed by machine learning
18. From individual elements to macroscopic materials: in search of new superconductors via machine learning10.1038/s41524-023-01023-6 ↗
19. Closed-loop superconducting materials discovery10.1038/s41524-023-01131-3 ↗
20. Cuprate superconducting materials above liquid nitrogen temperature from machine learning10.1039/D3RA02848H ↗
21. Machine learning for electrocatalyst and photocatalyst design and discovery10.1021/acs.chemrev.2c00061 ↗
22. Machine-learning-assisted discovery of high-efficient oxygen evolution electrocatalysts10.1021/acs.jpclett.2c02873 ↗
23. Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species10.1039/D3CY00587A ↗
24. Machine learning design of single-atom catalysts for nitrogen fixation10.1021/acsami.3c08535 ↗
25. Catalyst design with machine learning10.1038/s41560-022-01112-8 ↗
26. Interpretable discovery of semiconductors with machine learning10.1038/s41524-023-01066-9 ↗
27. Discovery of crystallizable organic semiconductors with machine learning10.1021/jacs.4c05245 ↗
28. Advances in the design and discovery of organic semiconductors aided by machine learning10.1146/annurev-matsci-080423-011746 ↗
29. Screening new Quaternary semiconductor heusler compounds by machine-learning methods10.1021/acs.chemmater.3c01323 ↗
30. Revolutionizing the future of smart materials: a review of 4D printing, design, optimization, and machine learning integration10.1002/admt.202401369 ↗
31. 3D printing of self-powered triboelectric intelligent system with enhanced output performance for material identification via machine learning10.1016/j.cej.2025.163790 ↗
32. Convergence of machine learning with microfluidics and metamaterials to build smart materials10.1007/s12008-023-01707-9 ↗
33. A high-accuracy, real-time, intelligent material perception system with a machine-learning-motivated pressure-sensitive electronic skin10.1016/j.matt.2022.02.016 ↗
34. Biomimetic intelligent thermal management materials: from nature‐inspired design to machine‐learning‐driven discovery
35. An adaptive framework to accelerate optimization of high flame retardant composites using machine learning
36. Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins
37. Advancing flame retardant prediction: a self-enforcing machine learning approach for small datasets10.1063/5.0152195 ↗
38. Practical machine learning model selection and interpretation for organophosphorus flame retardancy in epoxy resin10.1016/j.polymdegradstab.2025.111209 ↗
39. Machine learning-driven molecular generation for accelerated screening of high-performance flame retardants in epoxy resin composites10.1016/j.cej.2025.163946 ↗
40. Deeper insights into flame retardancy of polymers by interpretable, quantifiable, yet accurate machine-learning model10.1016/j.polymdegradstab.2024.110981 ↗
41. Inverse design in search of materials with target functionalities10.1038/s41570-018-0121 ↗
42. Crystal structure prediction in the context of inverse materials design10.1088/1674-4926/39/7/071004 ↗
43. Inverse design of soft materials via a deep learning–based evolutionary strategy10.1126/sciadv.abj6731 ↗
44. Inverse design of truss lattice materials with superior buckling resistance10.1038/s41524-022-00938-w ↗
45. Inverse design of semiconductor materials with deep generative models10.1039/D4TA02872D ↗
46. Machine learning-assisted precision inverse design research of ternary cathode materials: a new paradigm for material design10.1016/j.jcis.2024.11.104 ↗
47. Machine learning-based inverse design for single-phase high entropy alloys10.1063/5.0109491 ↗
48. Target-directed discovery for low melting point alloys via inverse design strategy
49. Inverse design of aluminium alloys using multi-targeted regression10.1007/s10853-023-09317-2 ↗
50. A machine learning based inverse design method for high-performance copper alloys
51. Reverse design of mg-zn-mn-sr-ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm10.1016/j.matdes.2025.114494 ↗
52. Machine-learning-based characterization and inverse design of metamaterials10.3390/ma17143512 ↗
53. Inverse design of TPMS piezoelectric metamaterial based on deep learning10.1016/j.mechmat.2024.105109 ↗
54. Artificial neural networks for inverse design of a semi-auxetic metamaterial10.1016/j.tws.2024.111927 ↗
55. Inverse design of materials by machine learning10.3390/ma15051811 ↗
56. Deep learning assisted inverse design of metamaterial microwave absorber10.1063/5.0171437 ↗
57. Developing neural networks for inverse design of chiral metamaterials
58. Safe and economical preparation of amino acid-derived bio-based triazine char-forming agent for efficient intumescent flame retardant polypropylene10.1016/j.conbuildmat.2025.141876 ↗