Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approach...
| Veröffentlicht in: | Langmuir : the ACS journal of surfaces and colloids. - 1992. - 40(2024), 7 vom: 20. Feb., Seite 3691-3701 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Langmuir : the ACS journal of surfaces and colloids |
| Schlagworte: | Journal Article |
| Online verfügbar |
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