Conformational flexibility is essential to the stimuli-responsive property of organic materials,but achieving the reversible molecular transformation is still challenging in functional materials for the high energy ba...Conformational flexibility is essential to the stimuli-responsive property of organic materials,but achieving the reversible molecular transformation is still challenging in functional materials for the high energy barriers and restriction by intermolecular interactions.Herein,through the incorporation of various steric hindrances into phenothiazine derivatives with different positions and quantities to tune the molecular conformations by adjustable repulsive forces,the folded angles gradually changed from 180°to 90°in 17 compounds.When the angle located at 112°with moderated steric effect,dynamic and reversible transformation of conformations under mechanical force has been achieved for the low energy barriers and mutually regulated molecular motions,resulting in both selfrecoverable and stimuli-responsive phosphorescence properties for the first time.It opened up a new way to realize the self-recovery property of organic materials,which can facilitate the multi-functional property of smart materials with the opened avenue for other fields with inspiration.展开更多
High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experime...High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experimentation fails to explore this multi-dimensional design space,where processing routes(PRs)and composition must be co-optimized to achieve superior strength.This study introduces a process-synergistic active learning(PSAL)framework leveraging a conditional Wasserstein autoencoder(c-WAE)to enable the data-efficient design.By encoding PRs as conditional variables,the PSAL framework reveals exceptional synergistic effects across diverse PRs,significantly outperforming single-process approaches.The process-aware latent representation facilitates the efficient exploration of potential compositions across multi-PRs simultaneously.Through iterative active learning cycles integrating machine learning predictions with experimental validations,ultimate tensile strength is greatly improved:459.8MPa for gravity casting with T6 heat treatment within three iterations and 220.5MPa for gravity casting with hot extrusion in a single iteration.This framework handles sparse datasets effectively,capturing complex processcomposition-property relationships and establishing a new paradigm for accelerated multi-objective material design.展开更多
基金supported by the National Natural Science Foundation of China(22122504,22235006)Foundation of Hubei Scientific Committee(2022BAA015 and 2022EHB010).
文摘Conformational flexibility is essential to the stimuli-responsive property of organic materials,but achieving the reversible molecular transformation is still challenging in functional materials for the high energy barriers and restriction by intermolecular interactions.Herein,through the incorporation of various steric hindrances into phenothiazine derivatives with different positions and quantities to tune the molecular conformations by adjustable repulsive forces,the folded angles gradually changed from 180°to 90°in 17 compounds.When the angle located at 112°with moderated steric effect,dynamic and reversible transformation of conformations under mechanical force has been achieved for the low energy barriers and mutually regulated molecular motions,resulting in both selfrecoverable and stimuli-responsive phosphorescence properties for the first time.It opened up a new way to realize the self-recovery property of organic materials,which can facilitate the multi-functional property of smart materials with the opened avenue for other fields with inspiration.
基金supported by the open subject of the State Key Laboratory of Powder Metallurgy,Central South University(SKLPM-KF-003)a grant from the National Natural Science Foundation of China(52471142 and 52301167)+4 种基金the National Youth Talent Program,Ministry of Industry and Information Technology of China(GQQNKP005)the open subject of the State Key Laboratory of Solidification Processing,Northwestern Polytechnical University(SKLSP202403)the National Natural Science Foundation of China(12302140)the Fundamental Research Funds for the Central Universities of China(sxzy012023213)'China Postdoctoral Science Foundation(2023M732794)Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(GZB20230575).
文摘High-strength Al-Si alloys are important lightweight materials,but their optimal design is hindered by scarce-imbalance data,and complex compositional-process-property relationships.Traditional trialand-error experimentation fails to explore this multi-dimensional design space,where processing routes(PRs)and composition must be co-optimized to achieve superior strength.This study introduces a process-synergistic active learning(PSAL)framework leveraging a conditional Wasserstein autoencoder(c-WAE)to enable the data-efficient design.By encoding PRs as conditional variables,the PSAL framework reveals exceptional synergistic effects across diverse PRs,significantly outperforming single-process approaches.The process-aware latent representation facilitates the efficient exploration of potential compositions across multi-PRs simultaneously.Through iterative active learning cycles integrating machine learning predictions with experimental validations,ultimate tensile strength is greatly improved:459.8MPa for gravity casting with T6 heat treatment within three iterations and 220.5MPa for gravity casting with hot extrusion in a single iteration.This framework handles sparse datasets effectively,capturing complex processcomposition-property relationships and establishing a new paradigm for accelerated multi-objective material design.
