2020 Fiscal Year Research-status Report
Inverse materials design by integrating transfer learning techniques into a Bayesian framework
| Project/Area Number |
18K18017
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
Wu Stephen 統計数理研究所, データ科学研究系, 准教授 (70804186)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Keywords | Transfer learning / Materials informatics / Polymer design / Open source software |
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| Outline of Annual Research Achievements |
In 2020, I have started the second round of design process for high thermal conductivity polymers. I focused more on actual industrial application, targeting a wider range of material properties based on the manufacturing needs, such as linear thermal expansion coefficient, dielectric constant, dielectric loss tangent, water absorption, etc. Meanwhile, I narrowed down the search of polymers to a few classes of polymers, such as polyimide and liquid crystal polymers. New predictive models that take into account of descriptors built from physical and chemical properties were developed to enhance the predictive power of models using purely fingerprints of molecules. As a result, new candidates were identified and now under-going synthetic path evaluation by my collaborators. Furthermore, the transfer learning technique developed in this study has been modified and applied to two other studies targeting microscopic images of crystal materials and concrete crack segmentation. Two peer-reviewed journal papers were published as a result. Although international travel was prohibited due to COVID19, I have participated in two online talks related to the extension projects of transfer learning.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
While the computational design of the second round of high thermal conductivity polymers has been progressing smoothly, the synthesis of new polymers has been delayed due to the COVID19 hazard. I was not able to visit my collaborators who can synthesize the polymers, therefore, the synthesis progress is delayed. Furthermore, the COVID19 situation also limited the supply of ingredients for the synthesis tasks. As a result, I used the extra time to modify and apply my transfer learning technique to two other problems targeting microscopic images of crystal materials and concrete crack segmentation.
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| Strategy for Future Research Activity |
In the coming fiscal year, the top priority is to push the progress of polymer synthesis. Bayesian optimization techniques can be applied to filter out a small subset of polymer candidates that are considered to be at the highest priority for synthesis. Reliable remote communication platform has been secured to ensure smooth communication with my collaborators in order to progress the synthesis plans. Meanwhile, I will begin building a larger publicly available data and model library of different classes of polymers to facilitate the use of data science methods in the industry of materials science. The final goal is to cover a wide range of industrial applications through inclusion of more material properties, such as dielectric constant or refractive index.
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| Causes of Carryover |
Due to COVID19, traveling is prohibition in 2020. In-person collaboration and presentation may be resumed in the next fiscal year, which will be the main use of the fund extended to the next year. Also, upon success of newly synthesized polymers, journal papers will be published which may require the use of the remaining funds.
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