From processing text to counterfactual steel design: a knowledge-graph-organized and reliability-aware machine learning framework
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
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (30)
Show source-provided references
1. Phase Transformation Temperature Prediction in Steels via Machine Learning
2. Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties10.1016/j.mlwa.2024.100531 ↗
3. Machine learning mechanical properties of steel sheets from an industrial production route10.1016/j.mtla.2023.101810 ↗
4. Machine learning for alloys10.1038/s41578-021-00340-w ↗
5. An optimized machine-learning model for mechanical properties prediction and domain knowledge clarification in quenched and tempered steels10.1016/j.jmrt.2023.03.215 ↗
6. A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels10.1016/j.jmst.2021.09.004 ↗
7. Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition10.1016/j.istruc.2023.03.085 ↗
8. A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels10.1016/j.jmatprotec.2022.117810 ↗
9. Machine learning study on time-temperature-transformation diagram of carbon and low-alloy steel10.1007/s42243-023-00932-6 ↗
10. A critical examination of robustness and generalizability of machine learning prediction of materials properties10.1038/s41524-023-01012-9 ↗
11. Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study10.1038/s41524-024-01316-4 ↗
12. Probing out-of-distribution generalization in machine learning for materials10.1038/s43246-024-00731-w ↗
13. Kononova · Opportunities and Challenges of Text Mining in Materials Research, iScience · 2021 · 24
14. Materials information extraction via automatically generated corpus10.1038/s41597-022-01492-2 ↗
15. Structured information extraction from scientific text with large language models10.1038/s41467-024-45563-x ↗
16. Accelerating materials language processing with large language models10.1038/s43246-024-00449-9 ↗
17. Materials graph ontology10.1016/j.matlet.2021.129836 ↗
18. PMD Core Ontology: Achieving semantic interoperability in materials science10.1016/j.matdes.2023.112603 ↗
19. MatKG: an autonomously generated knowledge graph in Material Science10.1038/s41597-024-03039-z ↗
20. Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery10.1007/s40192-022-00286-z ↗
21. Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review10.1039/D3MH00039G ↗
22. Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks10.1038/s41598-024-61189-x ↗
23. Bayesian neural networks for uncertainty quantification in data-driven materials modeling10.1016/j.cma.2021.114079 ↗
24. Machine learning for materials scientists: an introductory guide toward best practices10.1021/acs.chemmater.0c01907 ↗
25. Towards physics-informed explainable machine learning and causal models for materials research10.1016/j.commatsci.2023.112740 ↗
26. Counterfactual explanations and how to find them: literature review and benchmarking10.1007/s10618-022-00831-6 ↗
27. Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization10.1016/j.ijplas.2023.103776 ↗
28. Machine learning-enabled constrained multi-objective design of architected materials10.1038/s41467-023-42415-y ↗
29. A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses10.1038/s41524-023-00968-y ↗
30. Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy10.1038/s41467-023-38111-6 ↗