Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning
Abstract Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the...
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| Format: | Article |
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Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01682-7 |
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| author | Ching-Chien Chen Robert J. Appleton Saswat Mishra Kat Nykiel Alejandro Strachan |
| author_facet | Ching-Chien Chen Robert J. Appleton Saswat Mishra Kat Nykiel Alejandro Strachan |
| author_sort | Ching-Chien Chen |
| collection | DOAJ |
| description | Abstract Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved. |
| format | Article |
| id | doaj-art-a85fceaf3e7a483d8321e5a45b388e5c |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-a85fceaf3e7a483d8321e5a45b388e5c2025-08-20T03:22:53ZengNature Portfolionpj Computational Materials2057-39602025-06-011111910.1038/s41524-025-01682-7Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learningChing-Chien Chen0Robert J. Appleton1Saswat Mishra2Kat Nykiel3Alejandro Strachan4School of Materials Engineering, Purdue UniversitySchool of Materials Engineering, Purdue UniversitySchool of Materials Engineering, Purdue UniversitySchool of Materials Engineering, Purdue UniversitySchool of Materials Engineering, Purdue UniversityAbstract Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.https://doi.org/10.1038/s41524-025-01682-7 |
| spellingShingle | Ching-Chien Chen Robert J. Appleton Saswat Mishra Kat Nykiel Alejandro Strachan Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning npj Computational Materials |
| title | Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning |
| title_full | Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning |
| title_fullStr | Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning |
| title_full_unstemmed | Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning |
| title_short | Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning |
| title_sort | discovery of new high pressure phases integrating high throughput dft simulations graph neural networks and active learning |
| url | https://doi.org/10.1038/s41524-025-01682-7 |
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