Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vi...Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over time.This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy,ZX10.Utilizing rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal treatment.Employing Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion resistance.Additionally,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated variables.Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)Mg_(6)Zn_(3)phase in corrosion behavior.This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases.This thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.展开更多
Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar ...Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of options.An unguided search by experimental investigations or molecular simulations is prohibitively expensive.In this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or not.We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive search.We find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach.This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.展开更多
We investigate nanorod assemblies of two 64-substituted pentacenes, namely （2,3-X2-9,10-Y2）-substituted pentacenes with X -- Y = OCH3 （MOP） and with X = F, Y-- OCH3 （MOPF）, grown on Au（111） single crystals. By...We investigate nanorod assemblies of two 64-substituted pentacenes, namely （2,3-X2-9,10-Y2）-substituted pentacenes with X -- Y = OCH3 （MOP） and with X = F, Y-- OCH3 （MOPF）, grown on Au（111） single crystals. By using a multi-technique approach based on ultraviolet photoelectron spectroscopy X-ray photoelectron spectroscopy; and X-ray absorption, we find evidence for charge transfer screening at the interface with gold. Furthermore, the MOP and MOPF nanorods show a rough surface morphology, which was investigated with atomic force microscopy. We use molecular simulation techniques to investigate the energetic barriers to diffusion and to traverse step-edges to estimate their influence on the nanorod roughness. We find that barriers to surface diffusion on a terrace are anisotropic and that their direction favors the formation of nanorods in these materials.展开更多
基金supported by the National Science Foundation under grant DMR#2320355supported by the Department of Energy,Office of Science,Basic Energy Sciences,under Award#DESC0022305(formulation engineering of energy materials via multiscale learning spirals)Computing resources were provided by the ARCH high-performance computing(HPC)facility,which is supported by National Science Foundation(NSF)grant number OAC 1920103。
文摘Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over time.This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy,ZX10.Utilizing rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal treatment.Employing Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion resistance.Additionally,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated variables.Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)Mg_(6)Zn_(3)phase in corrosion behavior.This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases.This thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.
基金This work was partially supported by the Cornell Center for Materials Research with funding from the NSF MRSEC program(DMR-1719875)through a seed funding award.H.H.and M.P.were partially supported by this award.P.C.,P.F.,W.H.,and M.P.were partially supported by NSF(CMMI-1536895,DMR-1719875,DMR-1120296,CMMI-1254298)by AFOSR(FA9550-15-1-0038).
文摘Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of options.An unguided search by experimental investigations or molecular simulations is prohibitively expensive.In this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or not.We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive search.We find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach.This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.
文摘We investigate nanorod assemblies of two 64-substituted pentacenes, namely （2,3-X2-9,10-Y2）-substituted pentacenes with X -- Y = OCH3 （MOP） and with X = F, Y-- OCH3 （MOPF）, grown on Au（111） single crystals. By using a multi-technique approach based on ultraviolet photoelectron spectroscopy X-ray photoelectron spectroscopy; and X-ray absorption, we find evidence for charge transfer screening at the interface with gold. Furthermore, the MOP and MOPF nanorods show a rough surface morphology, which was investigated with atomic force microscopy. We use molecular simulation techniques to investigate the energetic barriers to diffusion and to traverse step-edges to estimate their influence on the nanorod roughness. We find that barriers to surface diffusion on a terrace are anisotropic and that their direction favors the formation of nanorods in these materials.
