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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.23471 |
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| _version_ | 1866916798480252928 |
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| author | Thelen, F. Lourens, F. Ludwig, A. |
| author_facet | Thelen, F. Lourens, F. Ludwig, A. |
| contents | Efficient characterization of surface compositions across high-dimensional materials spaces is critical for accelerating the discovery of surface-dominated functional materials. While X-ray photoelectron spectroscopy allows detailed surface composition investigation, it remains a time-intensive technique. In this work, it is demonstrated that Gaussian process regression can be used to accurately predict surface compositions from rapidly acquired volume composition data obtained by energy-dispersive X-ray spectroscopy, drastically reducing the number of required surface measurements. As a proof of principle, an exemplary system, the oxide Mg-Mn-Al-O, is synthesized as a composition-spread thin-film materials library and analyzed by high-throughput methods. We show that the surface composition of the entire library can be predicted with an accuracy of 96% with only 13 measurements, reducing the total measurement time by 277 h. This is a scalable and data-efficient solution for integrating surface analysis into materials discovery workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23471 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Surface Composition Characterization of Thin-Film Materials Libraries using Multi-Output Gaussian Process Regression Thelen, F. Lourens, F. Ludwig, A. Materials Science Efficient characterization of surface compositions across high-dimensional materials spaces is critical for accelerating the discovery of surface-dominated functional materials. While X-ray photoelectron spectroscopy allows detailed surface composition investigation, it remains a time-intensive technique. In this work, it is demonstrated that Gaussian process regression can be used to accurately predict surface compositions from rapidly acquired volume composition data obtained by energy-dispersive X-ray spectroscopy, drastically reducing the number of required surface measurements. As a proof of principle, an exemplary system, the oxide Mg-Mn-Al-O, is synthesized as a composition-spread thin-film materials library and analyzed by high-throughput methods. We show that the surface composition of the entire library can be predicted with an accuracy of 96% with only 13 measurements, reducing the total measurement time by 277 h. This is a scalable and data-efficient solution for integrating surface analysis into materials discovery workflows. |
| title | Accelerating Surface Composition Characterization of Thin-Film Materials Libraries using Multi-Output Gaussian Process Regression |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.23471 |