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Introducing hydroxyl groups to tailor the d-band center of Ir atom through side anchoring for boosted ORR and HER
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作者 Qing Lv Meiping Li +3 位作者 Xiaodong Li Xingru Yan zhufeng hou Changshui Huang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2024年第3期144-151,I0005,共9页
Tuning the coordination atoms of central metal is an effective means to improve the electrocatalytic activity of atomic catalysts.Herein,iridium(Ir) is proposed to be asymmetrically anchored by sp-N and pyridinic N of... Tuning the coordination atoms of central metal is an effective means to improve the electrocatalytic activity of atomic catalysts.Herein,iridium(Ir) is proposed to be asymmetrically anchored by sp-N and pyridinic N of hydrogen-substituted graphdiyne(HsGDY),and coordinated with OH as an Ir atomic catalyst(Ir_(1)-N-HsGDY).The electron structures,especially the d-band center of Ir atom,are optimized by these specific coordination atoms.Thus,the as-synthesized Ir_(1)-N-HsGDY exhibits excellent electrocatalytic performances for oxygen reduction and hydrogen evolution reactions in both acidic and alkaline media.Benefiting from the unique structure of HsGDY,IrN_(2)(OH)_(3) has been developed and demonstrated to act as the active site in these electrochemical reactions.All those indicate the fresh role of the sp-N in graphdiyne in producing a new anchor way and contributing to promote the electrocatalytic activity,showing a new strategy to design novel electrochemical catalysts. 展开更多
关键词 Oxygen reduction reaction D-band center Graphdiyne Hydroxyl group ELECTROCATALYSIS
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机器学习在材料设计方面的研究进展 被引量:10
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作者 孙中体 李珍珠 +3 位作者 程观剑 徐其琛 侯柱锋 尹万健 《科学通报》 EI CAS CSCD 北大核心 2019年第32期3270-3275,共6页
新材料的发现是推动现代科学发展与技术革新的源动力之一,是当前促进经济发展与解决环境问题的迫切需求.传统的材料研发基于试错法,效率低且成本高.大量实验与计算模拟产生的数据为新材料的研发提供了新契机.基于这些数据,机器学习最近... 新材料的发现是推动现代科学发展与技术革新的源动力之一,是当前促进经济发展与解决环境问题的迫切需求.传统的材料研发基于试错法,效率低且成本高.大量实验与计算模拟产生的数据为新材料的研发提供了新契机.基于这些数据,机器学习最近在材料性能预测、新材料的发现与设计等领域取得了很大进展.譬如基于材料项目(materials project)数据库对钙钛矿材料的统计分类、结合高通量计算对双钙钛矿卤化物材料稳定性的预测,以及金属间化合物电催化剂的设计与筛选等.除了基于隐式模型的预测,机器学习也可以用来发现具有物理可解释性的显式描述符,从而加速新材料的发现. 展开更多
关键词 机器学习 材料设计 能源转换 描述符
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Bayesian optimization based on a unified figure of merit for accelerated materials screening:A case study of halide perovskites 被引量:3
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作者 Xiwen Chen Chen Wang +2 位作者 Zhenzhu Li zhufeng hou Wan-Jian Yin 《Science China Materials》 SCIE EI CSCD 2020年第6期1024-1035,共12页
The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality.In the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening sol... The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality.In the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser limit.In this paper,the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability,optical absorption,and carrier lifetime.Multiple criteria make the quantitative description of material candidates difficult and computationally time-consuming.Taking halide perovskites as an example,we combine thermodynamic stability(ΔHd)and Eginto a unified figure of merit and use Bayesian optimization(BO)to accelerate materials screening.We have found that,in comparison to an exhaustive search via multiple parameters,BO based on the unified figure of merit can screen optimal candidates(E_g,PBEbetween 0.6–1.2 eV,ΔHd>-29 meV per atom)more efficiently.Therefore,the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost. 展开更多
关键词 DESCRIPTOR machine learning Bayesian optimization DFT calculations
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