Mechanical property prediction of random copolymers using uncertainty-based active learning
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Machine Learning and Algorithms · Machine Learning in Materials Science · Robot Manipulation and Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
Show source-provided references
1. Thermal conductivity and dielectric properties of bismaleimide/cyanate ester copolymer10.1049/hve.2017.0056 ↗
2. Thermoresponsive copolymers: from fundamental studies to applications10.1007/s00396-009-2028-x ↗
3. Mechanical properties and morphology of copolymer modified polymer blends
4. Styrene-butadiene rubbers
5. De novo design of a new class of “hard–soft” amorphous, microphase-separated, polyolefin block copolymer thermoplastic elastomers10.1021/acsmacrolett.5b00447 ↗
6. Sequence-defined bioactive macrocycles via an acid-catalysed cascade reaction10.1038/nchem.2508 ↗
7. Conformation-dependent sequence design (engineering) of AB copolymers10.1103/PhysRevLett.82.3456 ↗
8. Impact of copolymer sequence on solid-state properties for random, gradient and block copolymers containing thiophene and selenophene10.1021/ma301135n ↗
9. Sequence-defined multifunctional polyethers via liquid-phase synthesis with molecular sieving10.1038/s41557-018-0169-6 ↗
10. A review on the application of molecular descriptors and machine learning in polymer design10.1039/D3PY00395G ↗
11. New opportunity: machine learning for polymer materials design and discovery10.1002/adts.202100565 ↗
12. Polymer design using genetic algorithm and machine learning10.1016/j.commatsci.2020.110067 ↗
13. Machine learning strategy for accelerated design of polymer dielectrics10.1038/srep20952 ↗
14. Machine learning in polymer informatics10.1002/inf2.12167 ↗
15. Optimization of the elastic properties of block copolymers using coarse-grained simulation and an artificial neural network10.1016/j.commatsci.2022.111286 ↗
16. Cmal: Cost-effective multi-label active learning by querying subexamples10.1109/TKDE.2020.3003899 ↗
17. Multiscale computational solid mechanics: data and machine learning10.1093/jom/ufac037 ↗
18. Generating three-dimensional bioinspired microstructures using transformer-based generative adversarial network10.1016/j.jmrt.2023.10.200 ↗
19. Unsupervised learning of sequence-specific aggregation behavior for a model copolymer10.1039/D1SM01012C ↗
20. Designing sequence-specific copolymer compatibilizers using a molecular-dynamics-simulation-based genetic algorithm10.1021/acs.macromol.6b01747 ↗
21. Targeted sequence design within the coarse-grained polymer genome10.1126/sciadv.abc6216 ↗
22. J.Z. Bengar, J. van de Weijer, B. Twardowski, B. Raducanu, Reducing label effort: Self-supervised meets active learning, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 1631–1639.10.1109/ICCVW54120.2021.00188 ↗
23. Effective active learning strategy for multi-label learning10.1016/j.neucom.2017.08.001 ↗
24. Cost-effective active learning for deep image classification10.1109/TCSVT.2016.2589879 ↗
25. Active learning for multi-objective optimization
26. An automated machine learning-genetic algorithm framework with active learning for design optimization10.1115/1.4050489 ↗
27. Bias free multiobjective active learning for materials design and discovery10.1038/s41467-021-22437-0 ↗
28. Polymer sequence design via molecular simulation-based active learning10.1039/D2SM01193J ↗
29. Rasmussen · 2006
30. Gaussian process regression-based material model for stochastic structural analysis
31. Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels10.1038/s41598-020-74394-1 ↗
32. Predicting the material removal rate during electrical discharge diamond grinding using the Gaussian process regression: a comparison with the artificial neural network and response surface methodology10.1007/s00170-021-06701-7 ↗
33. Gaussian process regression for materials and molecules10.1021/acs.chemrev.1c00022 ↗
34. Octa (open computational tool for advanced material technology)
35. 2024
36. Phase diagram of random copolymers10.1103/PhysRevE.48.465 ↗
37. Computer simulations of nano-scale phenomena based on the dynamic density functional theories: Applications of SUSHI in the OCTA system
38. A general-purpose coarse-grained molecular dynamics program10.1016/S0010-4655(02)00271-0 ↗
39. An intuitive tutorial to Gaussian process regression10.1109/MCSE.2023.3342149 ↗
40. Scikit-learn: Machine learning in Python
41. 2024
42. Settles · 2009
43. Danka · 2018
44. 2024
45. McInnes · 2018
46. 2024
47. Multi-objective optimization of adhesive joint strength and elastic modulus of adhesive epoxy with active learning10.3390/ma17122866 ↗
48. Hierarchical multiresolution design of bioinspired structural composites using progressive reinforcement learning10.1002/adts.202200459 ↗