Artificial Intelligence Guided Thermoelectric Materials Design and Discovery
Abstract Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up ma...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2023-08-01
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| Series: | Advanced Electronic Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aelm.202300042 |
| _version_ | 1797745344732200960 |
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| author | Guangshuai Han Yixuan Sun Yining Feng Guang Lin Na Lu |
| author_facet | Guangshuai Han Yixuan Sun Yining Feng Guang Lin Na Lu |
| author_sort | Guangshuai Han |
| collection | DOAJ |
| description | Abstract Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials. |
| first_indexed | 2024-03-12T15:21:55Z |
| format | Article |
| id | doaj.art-82c4cf5223ea4624ad4f72dee8ca58ab |
| institution | Directory Open Access Journal |
| issn | 2199-160X |
| language | English |
| last_indexed | 2024-03-12T15:21:55Z |
| publishDate | 2023-08-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj.art-82c4cf5223ea4624ad4f72dee8ca58ab2023-08-11T02:16:17ZengWiley-VCHAdvanced Electronic Materials2199-160X2023-08-0198n/an/a10.1002/aelm.202300042Artificial Intelligence Guided Thermoelectric Materials Design and DiscoveryGuangshuai Han0Yixuan Sun1Yining Feng2Guang Lin3Na Lu4Lyles School of Civil Engineering Purdue University West Lafayette IN 47906 USASchool of Mechanical Engineering Purdue University West Lafayette IN 47906 USALyles School of Civil Engineering Purdue University West Lafayette IN 47906 USASchool of Mechanical Engineering Purdue University West Lafayette IN 47906 USALyles School of Civil Engineering Purdue University West Lafayette IN 47906 USAAbstract Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.https://doi.org/10.1002/aelm.202300042data‐driven materials discoverymachine learningsensitivity analysisthermoelectrics |
| spellingShingle | Guangshuai Han Yixuan Sun Yining Feng Guang Lin Na Lu Artificial Intelligence Guided Thermoelectric Materials Design and Discovery Advanced Electronic Materials data‐driven materials discovery machine learning sensitivity analysis thermoelectrics |
| title | Artificial Intelligence Guided Thermoelectric Materials Design and Discovery |
| title_full | Artificial Intelligence Guided Thermoelectric Materials Design and Discovery |
| title_fullStr | Artificial Intelligence Guided Thermoelectric Materials Design and Discovery |
| title_full_unstemmed | Artificial Intelligence Guided Thermoelectric Materials Design and Discovery |
| title_short | Artificial Intelligence Guided Thermoelectric Materials Design and Discovery |
| title_sort | artificial intelligence guided thermoelectric materials design and discovery |
| topic | data‐driven materials discovery machine learning sensitivity analysis thermoelectrics |
| url | https://doi.org/10.1002/aelm.202300042 |
| work_keys_str_mv | AT guangshuaihan artificialintelligenceguidedthermoelectricmaterialsdesignanddiscovery AT yixuansun artificialintelligenceguidedthermoelectricmaterialsdesignanddiscovery AT yiningfeng artificialintelligenceguidedthermoelectricmaterialsdesignanddiscovery AT guanglin artificialintelligenceguidedthermoelectricmaterialsdesignanddiscovery AT nalu artificialintelligenceguidedthermoelectricmaterialsdesignanddiscovery |