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.

该研究旨在通过使用通用神经网络潜力来分析界面。我们的目标是 N2 在 La0.5Ce0.5O1.75-x 还原平板上负载的 Runano 颗粒上的吸附和催化性能。这种异质系统表现出强金属支撑相互作用（SMSI），这是传统方法无法研究的。通过开发自动化程序，研究了 200 种不同催化剂配置的所有顶部位点的 N2 吸附特性，总共给出了 15600 种不同的吸附结果。进行统计分析以确定代表真实系统的催化剂结构。仔细讨论了活化势垒和局部结构之间的关系，以确定实验观察到的高活性背后的基本因素。为了进一步研究负载型纳米催化剂的界面性质，制备了不同金属和负载材料的组合。自动化程序进一步扩展，以探索其他小分子的吸附特性，这些特性是碳中性燃料合成、燃料电池中的氧还原反应和水电解中的氧析出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

Calculations of Real-System Nanoparticles Using Universal Neural Network Potential PFP

使用通用神经网络势 PFP 计算真实系统纳米颗粒

• 发表时间: 2021
• 期刊: arXiv
• 作者: Gerardo Valadez Huerta;Yusuke Nanba;Iori Kurata;Kosuke Nakago;So Takamoto;Chikashi Shinagawa;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