A Review of Performance Prediction Based on Machine Learning in Materials Science
With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, mult...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Nanomaterials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-4991/12/17/2957 |
| _version_ | 1827665405024403456 |
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| author | Ziyang Fu Weiyi Liu Chen Huang Tao Mei |
| author_facet | Ziyang Fu Weiyi Liu Chen Huang Tao Mei |
| author_sort | Ziyang Fu |
| collection | DOAJ |
| description | With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area. |
| first_indexed | 2024-03-10T01:27:45Z |
| format | Article |
| id | doaj.art-72d4297137284c399314ecde8c984f36 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-10T01:27:45Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-72d4297137284c399314ecde8c984f362023-11-23T13:48:28ZengMDPI AGNanomaterials2079-49912022-08-011217295710.3390/nano12172957A Review of Performance Prediction Based on Machine Learning in Materials ScienceZiyang Fu0Weiyi Liu1Chen Huang2Tao Mei3School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaSchool of Materials Science and Engineering, Hubei University, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaSchool of Materials Science and Engineering, Hubei University, Wuhan 430062, ChinaWith increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.https://www.mdpi.com/2079-4991/12/17/2957machine learningmaterials scienceperformance predictiondeep learning |
| spellingShingle | Ziyang Fu Weiyi Liu Chen Huang Tao Mei A Review of Performance Prediction Based on Machine Learning in Materials Science Nanomaterials machine learning materials science performance prediction deep learning |
| title | A Review of Performance Prediction Based on Machine Learning in Materials Science |
| title_full | A Review of Performance Prediction Based on Machine Learning in Materials Science |
| title_fullStr | A Review of Performance Prediction Based on Machine Learning in Materials Science |
| title_full_unstemmed | A Review of Performance Prediction Based on Machine Learning in Materials Science |
| title_short | A Review of Performance Prediction Based on Machine Learning in Materials Science |
| title_sort | review of performance prediction based on machine learning in materials science |
| topic | machine learning materials science performance prediction deep learning |
| url | https://www.mdpi.com/2079-4991/12/17/2957 |
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