The effects of hydrogen charging time and pressure on the hydrogen embrittlement(HE)susceptibility of X52 pipeline steel material are studied by slow strain rate tensile tests.The fracture morphologies of the specimen...The effects of hydrogen charging time and pressure on the hydrogen embrittlement(HE)susceptibility of X52 pipeline steel material are studied by slow strain rate tensile tests.The fracture morphologies of the specimens are observed by scanning electron microscopy.The HE susceptibility of the X52 pipeline steel material increases with an increase in both hydrogen charging time and hydrogen pressure.At a charging time of 96 h,the HE susceptibility index reaches 45.86%,approximately 3.6 times that at a charging time of 0 h.Similarly,a charging pressure of 4 MPa results in a HE susceptibility index of 31.61%,approximately 2.5 times higher than that at a charging pressure of 0.3 MPa.展开更多
Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into mate...Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors.However,the enormous computational costs and technical challenges of automatingcomputer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning.We developed SPACIER,an open-source software program that incorporates RadonPy,a Python library for fully automated polymer physical property calculations based on allatom classical molecular dynamics,into a Bayesian optimization-based polymer design system to overcome these challenges.As a proof-of-concept study,we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.展开更多
基金This work was supported by the National Key R&D Program of China(2021YFB4001601)the Youth Innovation Promotion Association CAS(2022187).
文摘The effects of hydrogen charging time and pressure on the hydrogen embrittlement(HE)susceptibility of X52 pipeline steel material are studied by slow strain rate tensile tests.The fracture morphologies of the specimens are observed by scanning electron microscopy.The HE susceptibility of the X52 pipeline steel material increases with an increase in both hydrogen charging time and hydrogen pressure.At a charging time of 96 h,the HE susceptibility index reaches 45.86%,approximately 3.6 times that at a charging time of 0 h.Similarly,a charging pressure of 4 MPa results in a HE susceptibility index of 31.61%,approximately 2.5 times higher than that at a charging pressure of 0.3 MPa.
基金support from MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(Project ID:hp210264)JST CREST(Grant Numbers JPMJCR19I3,JPMJCR22O3,JPMJCR2332)+4 种基金MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(19H05820)Grant-in-Aid for Scientific Research(A)(19H01132)Grant-in-Aid for Scientific Research(C)(22K11949)Computational resources were provided by Fugaku at the RIKEN Center for Computational Science,Kobe,Japan(hp210264)the supercomputer at the Research Center for Computational Science,Okazaki,Japan(project:23-IMS-C113,24-IMS-C107).
文摘Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems.First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors.However,the enormous computational costs and technical challenges of automatingcomputer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning.We developed SPACIER,an open-source software program that incorporates RadonPy,a Python library for fully automated polymer physical property calculations based on allatom classical molecular dynamics,into a Bayesian optimization-based polymer design system to overcome these challenges.As a proof-of-concept study,we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.
