Research progress in high-entropy alloys driven by high throughput computation and machine learning
High-entropy alloys have attracted great attention in various fields due to their high-entropy effect, severe lattice distortion, slow diffusion and special and excellent material performance due to the combination of various alloying elements in equal or near-equal molar proportions. Its high stren...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Journal of Materials Engineering
2023-03-01
|
| Series: | Cailiao gongcheng |
| Subjects: | |
| Online Access: | http://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2022.000997 |
| _version_ | 1797858880496074752 |
|---|---|
| author | ZHANG Cong LIU Jie XIE Shuyi XU Bin YIN Haiqing LIU Binbin QU Xuanhui |
| author_facet | ZHANG Cong LIU Jie XIE Shuyi XU Bin YIN Haiqing LIU Binbin QU Xuanhui |
| author_sort | ZHANG Cong |
| collection | DOAJ |
| description | High-entropy alloys have attracted great attention in various fields due to their high-entropy effect, severe lattice distortion, slow diffusion and special and excellent material performance due to the combination of various alloying elements in equal or near-equal molar proportions. Its high strength and hardness, fatigue resistance, excellent corrosion resistance, radiation resistance, near-zero thermal expansion coefficient, catalytic response, thermoelectric response and photoelectric conversion make high-entropy alloys have potential applications in many aspects. High-throughput computation and machine learning technology have rapidly become powerful tools to explore the huge composition space of high-entropy alloys and comprehensively predict material properties. The basic concepts of high-throughput computing and machine learning were introduced in this paper as well as the advantages of first-principles calculation, thermodynamic/kinetic calculation and machine learning in the research of high-entropy alloys. The application research status of high-entropy alloy composition screening, phase and microstructure calculations and performance prediction were summarized. In the final part, the existing problems, and the solutions and future prospects of this field were summarized, including developing tools for first-principles calculations and machine learning of high-entropy alloys, building high-quality databases for high-entropy alloys and integrating high-throughput computing with machine learning to globally optimize the mechanical property and service performance of high-entropy alloys. |
| first_indexed | 2024-04-09T21:20:33Z |
| format | Article |
| id | doaj.art-648826bbc39e452d925a0fab37e481b9 |
| institution | Directory Open Access Journal |
| issn | 1001-4381 |
| language | zho |
| last_indexed | 2024-04-09T21:20:33Z |
| publishDate | 2023-03-01 |
| publisher | Journal of Materials Engineering |
| record_format | Article |
| series | Cailiao gongcheng |
| spelling | doaj.art-648826bbc39e452d925a0fab37e481b92023-03-28T06:25:01ZzhoJournal of Materials EngineeringCailiao gongcheng1001-43812023-03-0151311610.11868/j.issn.1001-4381.2022.00099720230301Research progress in high-entropy alloys driven by high throughput computation and machine learningZHANG Cong0LIU Jie1XIE Shuyi2XU Bin3YIN Haiqing4LIU Binbin5QU Xuanhui6Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaCollaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, ChinaCollaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, ChinaCollaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaState Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaHigh-entropy alloys have attracted great attention in various fields due to their high-entropy effect, severe lattice distortion, slow diffusion and special and excellent material performance due to the combination of various alloying elements in equal or near-equal molar proportions. Its high strength and hardness, fatigue resistance, excellent corrosion resistance, radiation resistance, near-zero thermal expansion coefficient, catalytic response, thermoelectric response and photoelectric conversion make high-entropy alloys have potential applications in many aspects. High-throughput computation and machine learning technology have rapidly become powerful tools to explore the huge composition space of high-entropy alloys and comprehensively predict material properties. The basic concepts of high-throughput computing and machine learning were introduced in this paper as well as the advantages of first-principles calculation, thermodynamic/kinetic calculation and machine learning in the research of high-entropy alloys. The application research status of high-entropy alloy composition screening, phase and microstructure calculations and performance prediction were summarized. In the final part, the existing problems, and the solutions and future prospects of this field were summarized, including developing tools for first-principles calculations and machine learning of high-entropy alloys, building high-quality databases for high-entropy alloys and integrating high-throughput computing with machine learning to globally optimize the mechanical property and service performance of high-entropy alloys.http://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2022.000997high-entropy alloythermodynamicfirst principlesmachine learningperformance optimization |
| spellingShingle | ZHANG Cong LIU Jie XIE Shuyi XU Bin YIN Haiqing LIU Binbin QU Xuanhui Research progress in high-entropy alloys driven by high throughput computation and machine learning Cailiao gongcheng high-entropy alloy thermodynamic first principles machine learning performance optimization |
| title | Research progress in high-entropy alloys driven by high throughput computation and machine learning |
| title_full | Research progress in high-entropy alloys driven by high throughput computation and machine learning |
| title_fullStr | Research progress in high-entropy alloys driven by high throughput computation and machine learning |
| title_full_unstemmed | Research progress in high-entropy alloys driven by high throughput computation and machine learning |
| title_short | Research progress in high-entropy alloys driven by high throughput computation and machine learning |
| title_sort | research progress in high entropy alloys driven by high throughput computation and machine learning |
| topic | high-entropy alloy thermodynamic first principles machine learning performance optimization |
| url | http://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2022.000997 |
| work_keys_str_mv | AT zhangcong researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT liujie researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT xieshuyi researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT xubin researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT yinhaiqing researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT liubinbin researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning AT quxuanhui researchprogressinhighentropyalloysdrivenbyhighthroughputcomputationandmachinelearning |