The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,par...The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.展开更多
基金This work was supported in part by the“Materials Research by Information Integration”Initiative(MI2I)project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency(JST)and a Grant-in-Aid for Scientific Research(B)15H02672 from the Japan Society for the Promotion of Science(JSPS)S.W.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP18K18017+3 种基金K.H.gratefully acknowledges financial support from JSPS KAKENHI Grant Number JP17K17762a Grant-in-Aid for Scientific Research on Innovative Areas(16H06439)and PRESTO(JPMJPR16NA)C.S.gratefully acknowledges financial support from the Ministry of Education and Science of the Russian Federation(Grant 14.Y26.31.0019)J.M.acknowledges partial financial support by JSPS KAKENHI Grant Number JP16K06768.
文摘The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.
