GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys
Abstract High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01567-9 |
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| author | Mohamed Hendy Okan K. Orhan Homin Shin Ali Malek Mauricio Ponga |
| author_facet | Mohamed Hendy Okan K. Orhan Homin Shin Ali Malek Mauricio Ponga |
| author_sort | Mohamed Hendy |
| collection | DOAJ |
| description | Abstract High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this challenge, robust methods must be developed to efficiently explore the configurational space of HEA catalysts. Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape of HEAs. Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost, significantly accelerating configurational space sampling. However, APDFT errors increase substantially when permutations occur near binding sites. To address this issue, we developed a graph-based Gaussian process regression model to correct discrepancies between APDFT and conventional density functional theory values. Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV, requiring a handful of ab initio simulations. |
| format | Article |
| id | doaj-art-072fd2abf8d94bbfbe5dad272d2a8f00 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-072fd2abf8d94bbfbe5dad272d2a8f002025-08-20T03:07:43ZengNature Portfolionpj Computational Materials2057-39602025-04-0111111110.1038/s41524-025-01567-9GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloysMohamed Hendy0Okan K. Orhan1Homin Shin2Ali Malek3Mauricio Ponga4Department of Mechanical Engineering, University of British ColumbiaDepartment of Mechanical Engineering, University of British ColumbiaQuantum and Nanotechnologies Research Centre, National Research Council CanadaClean Energy Innovation Research Centre, National Research Council CanadaDepartment of Mechanical Engineering, University of British ColumbiaAbstract High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this challenge, robust methods must be developed to efficiently explore the configurational space of HEA catalysts. Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape of HEAs. Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost, significantly accelerating configurational space sampling. However, APDFT errors increase substantially when permutations occur near binding sites. To address this issue, we developed a graph-based Gaussian process regression model to correct discrepancies between APDFT and conventional density functional theory values. Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV, requiring a handful of ab initio simulations.https://doi.org/10.1038/s41524-025-01567-9 |
| spellingShingle | Mohamed Hendy Okan K. Orhan Homin Shin Ali Malek Mauricio Ponga GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys npj Computational Materials |
| title | GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys |
| title_full | GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys |
| title_fullStr | GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys |
| title_full_unstemmed | GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys |
| title_short | GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys |
| title_sort | gapf dft a graph based alchemical perturbation density functional theory for catalytic high entropy alloys |
| url | https://doi.org/10.1038/s41524-025-01567-9 |
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