Atomistic Insights into Interfacial Characteristics for Energy Conversion

对能量转换界面特性的原子洞察

• 批准号: 21F30701
• 负责人: 古山 通久
• 金额: $ 1.47万
• 依托单位: Shinshu University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-07-28 至 2023-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21F30701/
• 关键词: Neural Network Potential; Heterogeneous Catalysis; Interface; Molecular Simulation; Catalyst; Supported Nanoparticles; Dissociation

The study aims to analyze interfaces by using a universal neural network potential. We targeted the adsorption and catalytic properties of N2 on a Runanoparticle supported on a La0.5Ce0.5O1.75-x reduced slab. This heterogeneous system shows a strong-metal support interaction (SMSI), which cannot be investigated by conventional methods. By developing automated procedure, N2 adsorption properties on all ontop sites of 200 different catalyst configurations were investigated, giving 15600 different adsorption results in total. Statistical analyses was conducted to identify the catalyst structure representing the real-system. The relation between activation barrier and local structure was carefully discussed, to identify the essential factor behind the experimentally observed high activity.To further investigate the interfacial properties of supported nanocatalyst, combinations of different metal and support materials are prepared. Automated procedure was further extended to explore the adsorption properties of other small molecules, which are fundamental in key reactions such as carbon-neutral fuel synthesis, oxygen reduction reaction in fuel cells, and oxygen evolotion reaction in water electrolysis.

这项研究旨在利用通用神经网络势来分析界面。我们研究了氮气在La0.5Ce0.5O1.75-x还原平板上的吸附和催化性能。这种非均相体系表现出很强的金属-载体相互作用(SMSi)，这是常规方法无法研究的。通过开发自动化程序，考察了氮气在200种不同催化剂构型上的吸附性能，总共得到了15600种不同的吸附结果。对代表真实体系的催化剂结构进行了统计分析。为了进一步研究负载型纳米催化剂的界面性质，制备了不同金属和载体材料的组合。自动化程序进一步扩展到探索其他小分子的吸附性质，这些小分子是碳中性燃料合成、燃料电池中的氧还原反应和水电解中的析氧反应等关键反应的基础。

项目成果

First-Principles Calculations of Stability, Electronic Structure, and Sorption Properties of Nanoparticle Systems

纳米粒子系统稳定性、电子结构和吸附特性的第一性原理计算

• DOI: 10.2477/jccj.2021-0028
• 发表时间: 2021
• 期刊: Journal of Computer Chemistry, Japan
• 作者: Gerardo Valadez Huerta;Yusuke Nanba;Nor Diana Binti Zulkifli;David Samuel Rivera Rocabado;Takayoshi Ishimoto;Michihisa Koyama
• 通讯作者: Michihisa Koyama

Theoretical Investigation of N2 Adsorption on Supported Ru Nanoparticles on Partially Reduced La0.5Ce0.5O1.75 by Neural Network Potential Calculations

通过神经网络电位计算理论研究负载Ru纳米粒子在部分还原的La0.5Ce0.5O1.75上N2吸附

• 发表时间: 2021
• 作者: erardo Valadez Huerta;Katsutoshi Sato;Katsutoshi Nagaoka;Michihisa Koyama
• 通讯作者: Michihisa Koyama

Computer Automated Material Design by Universal Neural Network Potential

通过通用神经网络潜力进行计算机自动化材料设计

• 发表时间: 2022
• 作者: Gerardo Valadez Huerta;Ayako Tamura;Yusuke Nanba;Kaoru Hisama;Michihisa Koyama
• 通讯作者: Michihisa Koyama

Catalytic Properties of N2 on Ru/La0.5Ce0.5O1.75-x revealed by a Universal Neural Network Potential

通用神经网络势揭示 N2 在 Ru/La0.5Ce0.5O1.75-x 上的催化性能

• 发表时间: 2022
• 作者: G. Valadez Huerta;K. Hisama;K. Sato;K. Nagaoka;M. Koyama
• 通讯作者: M. Koyama

Application of Universal Neural Network Potential to Nitrogen Dissociation over Ru/La0.5Ce0.5O1.75-x for Ammonia Synthesis

通用神经网络势在 Ru/La0.5Ce0.5O1.75-x 氨合成中氮解离中的应用

• 发表时间: 2022
• 作者: G. Valadez Huerta;K. Hisama;M. Koyama
• 通讯作者: M. Koyama