Polymer backbone plays a fundamental role in determining the molecular architecture and properties of polymers.Polymers can be modified by incorporating heteroatoms into their backbones to achieve tunable optical and ...Polymer backbone plays a fundamental role in determining the molecular architecture and properties of polymers.Polymers can be modified by incorporating heteroatoms into their backbones to achieve tunable optical and mechanical properties,such as polyamide,polythioether and polysilane[1].The transition from nonconjugated to conjugated backbones of polymers challenges the traditional view of polymers as insulators,leading to the development of conductive polymers[2].In contrast,metal elements far surpass non-metal elements in both the variety of elemental types and the diversity of their outer-shell electrons,incorporating which into the polymer backbone is promising to create polymer materials with unique properties and applications,such as high mechanical strength and electrical conductivity[3].Integration of metal atoms into the polymer backbones has been reported.However,the lack of uniformity and continuity in the interaction of metal atoms limits electron transfer efficiency and hinders the full utilization of metal elements within polymer materials[4].To this end,a novel metal-backboned polymer was proposed[5,6],wherein the polymer backbone consists entirely of metal atoms interconnected through metal–metal bonds.This novel polymer was found with exceptional optical and electrical properties,showing promising applications in photoelectric devices,flexible electronics,and microwave absorption materials[7,8].展开更多
The large design space of metal-organic frameworks(MOFs)has prompted the utilization of deep learning to drive material design.Nonetheless,the prediction of key thermodynamic properties,such as heat of adsorption(ΔH_...The large design space of metal-organic frameworks(MOFs)has prompted the utilization of deep learning to drive material design.Nonetheless,the prediction of key thermodynamic properties,such as heat of adsorption(ΔH_(ads)),remains largely unexplored for CO_(2)adsorption in MOFs.Herein,we present IsothermNet,a high-throughput graph neural network designed to estimate uptake andΔH_(ads)over 0–50 bars,enabling high-quality full isotherm reconstruction(PCC:0.73–0.95[uptake],0.76–0.88[ΔH_(ads)]).We further bridged these adsorption properties to uptake behaviors(i.e.,isotherm shapes/types)and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance.This comparative analysis facilitated the discovery of a(1)physically-interpretable and(2)analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed,accessible textural information and extrinsic adsorption properties.When used cooperatively with IsothermNet,these descriptors enable efficient material screening,accelerating high-performance MOF discovery for CO_(2)capture.展开更多
基金supported by the Ministry of Science and Technology of the People’s Republic of China(2022YFA1203001 and 2022YFA1203002)the National Natural Science Foundation of China(T2321003,22335003,and 22105045)Science and Technology Commission of Shanghai Municipality(21511104900 and 20JC1414902).
文摘Polymer backbone plays a fundamental role in determining the molecular architecture and properties of polymers.Polymers can be modified by incorporating heteroatoms into their backbones to achieve tunable optical and mechanical properties,such as polyamide,polythioether and polysilane[1].The transition from nonconjugated to conjugated backbones of polymers challenges the traditional view of polymers as insulators,leading to the development of conductive polymers[2].In contrast,metal elements far surpass non-metal elements in both the variety of elemental types and the diversity of their outer-shell electrons,incorporating which into the polymer backbone is promising to create polymer materials with unique properties and applications,such as high mechanical strength and electrical conductivity[3].Integration of metal atoms into the polymer backbones has been reported.However,the lack of uniformity and continuity in the interaction of metal atoms limits electron transfer efficiency and hinders the full utilization of metal elements within polymer materials[4].To this end,a novel metal-backboned polymer was proposed[5,6],wherein the polymer backbone consists entirely of metal atoms interconnected through metal–metal bonds.This novel polymer was found with exceptional optical and electrical properties,showing promising applications in photoelectric devices,flexible electronics,and microwave absorption materials[7,8].
基金This work used the Engaging OnDemand clusters at MIT Office of Research Computing and Data(ORCD).This work additionally used Bridges-2 at Pittsburgh Supercomputing Center(PSC)through allocation MCH230021 from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)program,which is supported by National Science Foundation Grants No.2138259,2138286,2138307,2137603,and 2138296EL is supported by the National Science Foundation Graduate Research Fellowship under Grant No.2141064YZ is supported by the MIT Evergreen Graduate Innovation Fellowship.
文摘The large design space of metal-organic frameworks(MOFs)has prompted the utilization of deep learning to drive material design.Nonetheless,the prediction of key thermodynamic properties,such as heat of adsorption(ΔH_(ads)),remains largely unexplored for CO_(2)adsorption in MOFs.Herein,we present IsothermNet,a high-throughput graph neural network designed to estimate uptake andΔH_(ads)over 0–50 bars,enabling high-quality full isotherm reconstruction(PCC:0.73–0.95[uptake],0.76–0.88[ΔH_(ads)]).We further bridged these adsorption properties to uptake behaviors(i.e.,isotherm shapes/types)and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance.This comparative analysis facilitated the discovery of a(1)physically-interpretable and(2)analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed,accessible textural information and extrinsic adsorption properties.When used cooperatively with IsothermNet,these descriptors enable efficient material screening,accelerating high-performance MOF discovery for CO_(2)capture.
