The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,...The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,the design of biodegradable polymers to overcome these issues is often difficult.In this study,we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 andα-amino acid segments that are mechanically tough and degradable.Multi-objective optimization based on Gaussian process regression for the degradation rate,strain at break,and Young’s modulus(the last two parameters correspond to toughness)suggested appropriateα-amino acid sequences for polyamides endowed with both properties.Ridge regression revealed that the physical factors associated with the sequences,as well as the higher-order multiblock-derived structures(such as the crystal lattice structure,melting points,and hydrogen bonding),were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data.Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.展开更多
基金supported by the Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), and “Technologies for Smart Bio-industry and Agriculture” (funding agency: Bio-oriented Technology Research Advancement Institution, NARO)This study is based on the results obtained from project JPNP18016, commissioned by the New Energy and Industrial Technology Development Organization (NEDO)+4 种基金This work was also supported by JSPS Grant-in-Aid for Scientific Research on Innovative Areas, Discrete Geometric Analysis for Materials Design: 20H04644, Grant-in-Aid for Scientific Research (B): 20H02800Data Creation and Utilization Type Material Research and Development Project Grant Numbers JPMXP1122683430 and JPMXP1122714694by Institute of Mathematics for Industry, Joint Usage/Research Center in Kyushu University (Workshop (II), Reference No. 2023a011 and 2024a011)Y.A. and K.T. acknowledge the financial support from the Grant-in-Aid for the RIKEN-Kyushu University Science and Technology Hub Collaborative Research ProgramSynchrotron radiation experiments were performed at the BL40XU and BL05XU beamlines of SPring-8 with the approval of the Japan Synchrotron Radiation Research Institute (JASRI) (Proposal Nos. 2020A1525, 2021B1476, and 2022B1029).
文摘The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,the design of biodegradable polymers to overcome these issues is often difficult.In this study,we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 andα-amino acid segments that are mechanically tough and degradable.Multi-objective optimization based on Gaussian process regression for the degradation rate,strain at break,and Young’s modulus(the last two parameters correspond to toughness)suggested appropriateα-amino acid sequences for polyamides endowed with both properties.Ridge regression revealed that the physical factors associated with the sequences,as well as the higher-order multiblock-derived structures(such as the crystal lattice structure,melting points,and hydrogen bonding),were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data.Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.
