Research Using Matlantis


Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential

Kaoru Hisama, Gerardo Valadez Huerta, Michihisa Koyama

Ionic transport under an electric field bias is the fundamental component of electrochemical devices and processes. A universal neural network potential with Bader charge prediction is integrated into a Langevin thermostat NVT simulation to realize a direct simulation to examine those ion dynamics under an external electric field that is scalable and adaptable for various systems. We calculated the ion conductivity of O2− ions in yttria-stabilized zirconia (YSZ) and protons in hydrochloric acid water solution. The conductivity of YSZ shows a tendency consistent with the one using a Buckingham potential tuned for the system. For HClaq, the proton hopping contributes to the higher conductivity of protons than the counter anion Cl−, suggesting that our method is a promising tool for the ionic system, including chemical reactions and either for solid or liquid.


Cyber Catalysis: N2 Dissociation over Ruthenium Catalyst with Strong Metal-Support Interaction

Gerardo Valadez Huerta, Kaoru Hisama, Katsutoshi Sato, Katsutoshi Nagaoka, Michihisa Koyama

Catalysis informatics is constantly developing, and significant advances in data mining, molecular simulation, and automation for computational design and highthroughput experimentation have been achieved. However, efforts to reveal the mechanisms of complex supported nanoparticle catalysts in cyberspace have proven to be unsuccessful thus far. This study fills this gap by exploring N2 dissociation on a supported Ru nanoparticle as an example using a universal neural network potential. We calculated 200 catalyst configurations consideringthe reduction of the support and strong metal-support interaction (SMSI), eventually performing 15,600 calculations for various N2 adsorption states. After successfully validating our results with experimental IR spectral data, we clarified key N2 dissociation pathways behind the high activity of the SMSI surface and disclosed the maximum activity of catalysts reduced at 650 °C. Our method is well applicable to other complex systems, and we believe it represents a key first step toward the digital transformation of investigations on heterogeneous catalysis.


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

Gerardo Valadez Huerta, Yusuke Nanba, Iori Kurata, Kosuke Nakago, So Takamoto, Chikashi Shinagawa, Michihisa Koyama

It is essential to explore the stability and activity of real-system nanoparticles theoretically. While applications of theoretical methods for this purpose can be found in literature, the expensive computational costs of conventional theoretical methods hinder their massive applications to practical materials design. With the recent development of neural network algorithms along with the advancement of computer systems, neural network potentials have emerged as a promising candidate for the description of a wide range of materials, including metals and molecules, with a reasonable computational time. In this study, we successfully validate a universal neural network potential, PFP, for the description of monometallic Ru nanoparticles, PdRuCu ternary alloy nanoparticles, and the NO adsorption on Rh nanoparticles against first-principles calculations. We further conduct molecular dynamics simulations on the NO-Rh system and challenge the PFP to describe a large, supported Pt nanoparticle system.