2021 Fiscal Year Annual Research Report
Atomistic Insights into Interfacial Characteristics for Energy Conversion
| Project/Area Number |
21F30701
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| Research Institution | Shinshu University |
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Principal Investigator |
古山 通久 信州大学, 先鋭領域融合研究群先鋭材料研究所, 教授(特定雇用) (60372306)
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| Co-Investigator(Kenkyū-buntansha) |
VALADEZ HUERTA GERARDO 信州大学, 先鋭領域融合研究群先鋭材料研究所, 外国人特別研究員
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| Project Period (FY) |
2021-07-28 – 2023-03-31
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| Keywords | Neural Network Potential / Heterogeneous Catalysis / Interface / Molecular Simulation / Catalyst / Supported Nanoparticles / Dissociation |
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| Outline of Annual Research Achievements |
The study aims to analyze interfaces with reactive molecular simulations. We decided to adopt a universal neural network potential for this study. This model is capable of describing chemical reactions and predicting partial charges, and it considers up to 55 chemical elements. The model’s capability to describe interfacial systems was thoroughly tested during the project. The tests were conducted for alloy nanoparticles, molecular adsorption on monometallic nanoparticles, and metal-oxides. We targeted the adsorption and catalytic properties of N2 on a Ru nanoparticle supported on a La0.5Ce0.5O1.75-x reduced slab. This heterogeneous system shows a strong-metal support interaction (SMSI). Such complex systems cannot be assessed with conventional methods, usually applied for heterogeneous catalysis. An automation procedure was implemented, which allows characterizing the adsorption behavior of diatomic molecules on all nanoparticle on-top sites for different configurations with various support reduction and SMSI degrees. We considered 200 catalysts resulting in 15600 calculated adsorption sites. The activation barrier for the dissociation of N2 in such a complex heterogeneous system was approached. The calculations and results are unique in their type.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Applying the universal neural network potential makes the parameterization of the conventional reactive force field like ReaxFF unnecessary. However, this new method was not applied to complex interfacial system so far. Therefore, a rigorous check must be conducted before beginning the massive use in the project. Nevertheless, rapid computation realized by the universal neural network potential saves the total research resource much, resulting in the advances and achievements more than expected. Thus, we believe that the progress is generally favorable.
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| Strategy for Future Research Activity |
In the first phase of the project, we could show the capability of using a universal neural network potential to describe heterogeneous systems. However, we could also identify the limits of the model. Nevertheless, the next step is to analyze further supported nanoparticle systems with the developed automation tools to create a database for heterogeneous catalysis. We also seek to conduct a first calculation, e.g., for the complete ammonia synthesis reaction path on a supported nanoparticle with the strong metal support interaction to assess the activation barriers for each reaction step on different catalyst sites.
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