Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911870408982528 |
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| author | Huang, Xiang Ju, Shenghong |
| author_facet | Huang, Xiang Ju, Shenghong |
| contents | Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer datasets, property calculators, feature engineering and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity Huang, Xiang Ju, Shenghong Soft Condensed Matter Materials Science Applied Physics Chemical Physics Computational Physics Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer datasets, property calculators, feature engineering and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic. |
| title | Tutorial: AI-assisted exploration and active design of polymers with high intrinsic thermal conductivity |
| topic | Soft Condensed Matter Materials Science Applied Physics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2403.15887 |