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| 1 | Machine learning guided appraisal and exploration of phase design for high entropy alloys显示文摘High entropy alloys(HEAs)and compositionally complex alloys(CCAs)have recently attracted great research interest because of their remarkable mechanical and physical properties.Although many useful HEAs or CCAs were reported,the rules of phase design,if there are any,which could guide alloy screening are still an open issue.In this work,we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning(ML)algorithms.Based on the artificial neural network algorithm,we were able to derive and extract a sensitivity matrix from the ML modeling,which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase,such as solid solution,intermetallic,or amorphous phase.Furthermore,we explored the use of an extended set of new design parameters,which had not been considered before,for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule,we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system.The outcomes of our experiments agree reasonably well with our predictions,which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs. | Ziqing Zhou Yeju Zhou Quanfeng He Zhaoyi Ding Fucheng Li Yong Yang | 2019 | npj Computational Materials2019，，1： | 16 |
| 2 | Exploring the design of eutectic or near-eutectic multicomponent alloys: From binary to high entropy alloys显示文摘Eutectic and near-eutectic high entropy alloys(HEAs) have recently attracted a great deal of interest because of their promising properties, such as an excellent castability and unique combination of good ductility and high strength. However, in the absence of a phase diagram, it remains a non-trivial task to find a eutectic or near-eutectic composition for a HE A system, which usually demands a tremendous amount of efforts if a trial-and-error approach is followed. In this paper, we briefly review the thermodynamics that governs the eutectic solidification in regular binary and ternary alloys, and proceed to the discussion for the design of eutectic HEAs. Based on the data reported, we then propose an improved strategy which may enable an efficient search for the eutectic or near eutectic HEA compositions. | ding zhaoyi he quanfeng yang yong | 2018 | Science China(Technological Sciences)2018，61，2： | 4 |
| 3 | Electrochemical hydrogen-storage capacity of graphene can achieve a carbon-hydrogen atomic ratio of 1:1显示文摘As a promising hydrogen-storage material,graphene is expected to have a theoretical capacity of 7.7 wt%,which means a carbon-hydrogen atomic ratio of 1:1.However,it has not been demonstrated yet by experiment,and the aim of the U.S.Department of Energy is to achieve 5.5 wt%in 2025.We designed a spatially-confined electrochemical system and found that the storage capacity of hydrogen adatoms on single layer graphene(SLG)is as high as 7.3 wt%,which indicates a carbon-hydrogen atomic ratio of 1:1 by considering the sp^(3) defects of SLG.First,SLG was deposited on a large-area polycrystalline platinum(Pt)foil by chemical vapor deposition(CVD);then,a micropipette with reference electrode,counter electrode and electrolyte solution inside was impacted on the SLG/Pt foil(the working electrode)to construct the spatially-confined electrochemical system.The SLG-uncovered Pt atoms act as the catalytic sites to convert protons(H^(+))to hydrogen adatoms(H_(ad)),which then spill over and are chemically adsorbed on SLG through surface diffusion during the cathodic scan.Because the electrode processes are reversible,the H_(ad) amount can be measured by the anodic stripping charge.This is the first experimental evidence for the theoretically expected hydrogen-storage capacity on graphene at ambient environment,especially by using H+rather than hydrogen gas(H_(2))as the hydrogen source,which is of significance for the practical utilization of hydrogen energy. | Quanfeng He Lanping Zeng Lianhuan Han Juan Peng Matthew M.Sartin Yuan-Zhi Tan Dongping Zhan Zhong-Qun Tian | 2022 | Science China Chemistry2022，65，2： | 2 |
| 4 | Hierarchically porous Co@N-doped carbon fiber assembled by MOF-derived hollow polyhedrons enables effective electronic/mass transport:An advanced 1D oxygen reduction catalyst for Zn-air battery显示文摘Developing advanced oxygen reduction reaction(ORR)electrocatalysts with rapid mass/electron transport as well as conducting relevant kinetics investigations is essential for energy technologies,but both still face ongoing challenges.Herein,a facile approach was reported for achieving the highly dispersed Co nanoparticles anchored hierarchically porous N-doped carbon fibers(Co@N-HPCFs),which were assembled by core-shell MOFs-derived hollow polyhedrons.Notably,the unique one-dimensional(1D)carbon fibers with hierarchical porosity can effectively improve the exposure of active sites and facilitate the electron transfer and mass transfer,resulting in the enhanced reaction kinetics.As a result,the ORR performance of the optimal Co@N-HPCF catalysts remarkably outperforms that of commercial Pt/C in alkaline solution,reaching a limited diffusion current density(J)of 5.85 m A cm^(-2)and a half-wave potential(E_(1/2))of 0.831 V.Particularly,the prepared Co@N-HPCF catalysts can be used as an excellent air-cathode for liquid/solid-state Zn-air batteries,exhibiting great potentiality in portable/wearable energy devices.Furthermore,the reaction kinetic during ORR process is deeply explored by finite element simulation,so as to intuitively grasp the kinetic control region,diffusion control region,and mixing control region of the ORR process,and accurately obtain the relevant kinetic parameters.This work offers an effective strategy and a reliable theoretical basis for the engineering of first-class ORR electrocatalysts with fast electronic/mass transport. | Yifei Zhang Quanfeng He Zihao Chen Yuqing Chi Junwei Sun Ding Yuan Lixue Zhang | 2023 | Journal of Energy Chemistry2023，，1： | 0 |
| 5 | Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys显示文摘The chemical element distributions always strongly affect the deformation mechanisms and mechanical properties of alloying materials.However,the detailed atomic origin still remains unknown in highentropy alloys(HEAs)with a stable random solid solution.Here,considering the effect of elemental fluctuation distribution,the deformation behavior and mechanical response of the widely-studied equimolar random Co Cr Fe Mn Ni HEA are investigated by atomic simulations combined with machine learning and micro-pillar compression experiments.The elemental anisotropy factor is proposed,and then used to evaluate the chemical element distribution.The experimental and simulation results show that the local variations of chemical compositions exist and play a critical role in the deformation partitioning and mechanical properties.The high strength and good plasticity of HEAs are obtained via tuning the chemical element distributions,and the optimal elemental anisotropy factor ranges from 2.9 to 3 using machine learning.This trend can be attributed to the cooperative mechanisms depending on the local variational composition:massive partial dislocation multiplication at an initial stage of plastic deformation,and the inhibition of localized shear banding via the nucleation of deformation twinning at a later stage.Using the new insights gained here,it would be possible to create new metallic alloys with superior properties through thermal-mechanical treatment to tailoring the chemical element distribution. | Jia Li Baobin Xie Quanfeng He Bin Liu Xin Zeng Peter KLiaw Qihong Fang Yong Yang Yong Liu | 2022 | Journal of Materials Science & Technology2022，，25： | 0 |
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