High-throughput validation of phase formability and simulation accuracy of Cantor alloys
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918216610086912 |
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| author | Cheng, Changjun Persaud, Daniel Li, Kangming Moorehead, Michael J. Page, Natalie Lavoie, Christian Moreno, Beatriz Diaz Couet, Adrien Lofland, Samuel E Hattrick-Simpers, Jason |
| author_facet | Cheng, Changjun Persaud, Daniel Li, Kangming Moorehead, Michael J. Page, Natalie Lavoie, Christian Moreno, Beatriz Diaz Couet, Adrien Lofland, Samuel E Hattrick-Simpers, Jason |
| contents | High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19335 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-throughput validation of phase formability and simulation accuracy of Cantor alloys Cheng, Changjun Persaud, Daniel Li, Kangming Moorehead, Michael J. Page, Natalie Lavoie, Christian Moreno, Beatriz Diaz Couet, Adrien Lofland, Samuel E Hattrick-Simpers, Jason Materials Science Machine Learning High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement. |
| title | High-throughput validation of phase formability and simulation accuracy of Cantor alloys |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2511.19335 |