Advances in high-pressure materials discovery enabled by machine learning
Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenge...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-05-01
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| Series: | Matter and Radiation at Extremes |
| Online Access: | http://dx.doi.org/10.1063/5.0255385 |
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| _version_ | 1849403346303057920 |
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| author | Zhenyu Wang Xiaoshan Luo Qingchang Wang Heng Ge Pengyue Gao Wei Zhang Jian Lv Yanchao Wang |
| author_facet | Zhenyu Wang Xiaoshan Luo Qingchang Wang Heng Ge Pengyue Gao Wei Zhang Jian Lv Yanchao Wang |
| author_sort | Zhenyu Wang |
| collection | DOAJ |
| description | Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenges related to computational efficiency and scalability, particularly when applied to complex systems. Recent advances in machine learning (ML) have shown tremendous promise in addressing these limitations, enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions. This review provides a concise overview of recent progress in ML-assisted CSP methodologies, with a particular focus on machine learning potentials and generative models. By critically analyzing these advances, we highlight the transformative impact of ML in accelerating materials discovery, enhancing computational efficiency, and broadening the applicability of CSP. Additionally, we discuss emerging opportunities and challenges in this rapidly evolving field. |
| format | Article |
| id | doaj-art-d32ebd728b934f5aa76da1e5e0508f5c |
| institution | Kabale University |
| issn | 2468-080X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | Matter and Radiation at Extremes |
| spelling | doaj-art-d32ebd728b934f5aa76da1e5e0508f5c2025-08-20T03:37:17ZengAIP Publishing LLCMatter and Radiation at Extremes2468-080X2025-05-01103033801033801-910.1063/5.0255385Advances in high-pressure materials discovery enabled by machine learningZhenyu Wang0Xiaoshan Luo1Qingchang Wang2Heng Ge3Pengyue Gao4Wei Zhang5Jian Lv6Yanchao Wang7Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaKey Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun 130012, People’s Republic of ChinaCrystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays a critical role in materials science, traditional approaches often encounter significant challenges related to computational efficiency and scalability, particularly when applied to complex systems. Recent advances in machine learning (ML) have shown tremendous promise in addressing these limitations, enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions. This review provides a concise overview of recent progress in ML-assisted CSP methodologies, with a particular focus on machine learning potentials and generative models. By critically analyzing these advances, we highlight the transformative impact of ML in accelerating materials discovery, enhancing computational efficiency, and broadening the applicability of CSP. Additionally, we discuss emerging opportunities and challenges in this rapidly evolving field.http://dx.doi.org/10.1063/5.0255385 |
| spellingShingle | Zhenyu Wang Xiaoshan Luo Qingchang Wang Heng Ge Pengyue Gao Wei Zhang Jian Lv Yanchao Wang Advances in high-pressure materials discovery enabled by machine learning Matter and Radiation at Extremes |
| title | Advances in high-pressure materials discovery enabled by machine learning |
| title_full | Advances in high-pressure materials discovery enabled by machine learning |
| title_fullStr | Advances in high-pressure materials discovery enabled by machine learning |
| title_full_unstemmed | Advances in high-pressure materials discovery enabled by machine learning |
| title_short | Advances in high-pressure materials discovery enabled by machine learning |
| title_sort | advances in high pressure materials discovery enabled by machine learning |
| url | http://dx.doi.org/10.1063/5.0255385 |
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