Publication
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
Machine learning technology has a great potential to accelerate the discovery of innovative functional materials. We demonstrate a successful discovery of novel high thermal conductivity polymers that was inspired by machine-learning-assisted polymer chemistry. The achievement was made possible by the interplay of a machine intelligence trained with a polymer database called PoLyInfo. Using a Bayesian molecular design algorithm trained to recognize quantitative structure-property relationships on thermal conductivity and other targeted polymeric properties, we identified thousands of promising designed polymers. Three were selected for monomer synthesis and polymerization based on chemical insights on their potential for further processing in real applications.
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- 21/06/2019
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41524_2019_203_MOESM2_ESM.pdf | 1.48 MB | MDR Open |
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s41524-019-0203-2.pdf | 2.25 MB | MDR Open |
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