Research Using Matlantis

Increasing the Sodium Metal Electrode Compatibility with the Na3PS4 Solid-State Electrolyte through Heteroatom Substitution

Lieven Bekaert, Suzuno Akatsuka, Naoto Tanibata, Frank De Proft, Annick Hubin, Mesfin Haile Mamme, Masanobu Nakayama

Rechargeable batteries are essential to the global shift towards renewable energy sources and their storage. At present, improvements in their safety and sustainability are of great importance as part of global sustainable development goals. A major contender in this shift are rechargeable solid-state sodium batteries, as a low-cost, safe, and sustainable alternative to conventional lithium-ion batteries. Recently, solid-state electrolytes with a high ionic conductivity and low flammability have been developed. However, these still face challenges with the highly reactive sodium metal electrode. The study of these electrolyte-electrode interfaces is challenging from a computational and experimental point of view, but recent advances in molecular dynamics neural-network potentials are finally enabling access to these environments compared to more computationally expensive conventional ab-initio techniques. In this study, heteroatom-substituted Na3PS3X1 analogues, where X is sulfur, oxygen, selenium, tellurium, nitrogen, chlorine, and fluorine, are investigated using total-trajectory analysis and neural-network molecular dynamics. It was found that inductive electron-withdrawing and electron-donating effects, alongside differences in heteroatom atomic radius, electronegativity, and valency, influenced the electrolyte reactivity. The Na3PS3O1 oxygen analogue was found to have superior chemical stability against the sodium metal electrode, paving the way towards high-performance, long lifetime and reliable rechargeable solid-state sodium batteries.

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CO Adsorption on Ternary Nanoalloys by Universal Neural Network Potential

Ayako TAMURA, Gerardo VALADEZ HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA

Multi-element alloy nanoparticles have attracted attention for their potentially high catalytic properties. However, a high degree of freedom in configurations of metal atoms within nanoparticle increases the distinct adsorption sites, making it difficult to theoretically analyze its catalytic properties because the first-principles calculation requires a considerable computational cost. In this study, we develop a sequential scheme to calculate hundreds of adsorption sites by employing a pre-trained universal neural network potential named PFP. Our automated scheme is applied to CO single-molecule adsorption of CO onto PtRuIr ternary alloy nanoparticles. The calculation results are first compared with DFT results to confirm the accuracy. Adsorption energies in the alloy systems are widely distributed in comparison with those of the monometal counterparts, indicating that the alloy nanoparticle includes adsorption sites with various catalytic activities.

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Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and ‘Ugly Duckling’ Dilemma

Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi, Kenichi Oyaizu

Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current methodologies don't entirely bypass this theorem and may lead to decreased accuracy with unfamiliar data. We propose using Open AI GPT-4 language model for explanatory variable selection, leveraging its extensive knowledge and logical reasoning capabilities to embed domain knowledge in tasks predicting structure-property correlations, such as the refractive index of polymers. This can partially overcome challenges posed by the "Ugly Duckling" theorem and limited data availability.

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On the Thermodynamic Stability of Alloys: Combination of Neural Network Potential and Wang-Landau Sampling

Tien Quang Nguyen, Yusuke Nanba, and Michihisa Koyama

Thermodynamic properties and atomic configuration of PdRu alloy were investigated using Wang-Landau Monte Carlo method in combination with a newly-developed universal neural network potential. By using this new potential, excess energy of PdRu alloy was calculated. It is found that PdRu alloy in FCC lattice is unstable in the full range of alloy composition. This agrees with previous study based on density functional theory. The combined method was able to determine the configurational density of states, from which thermodynamic properties of the alloy were derived. It is found that when temperature increases, the excess free energy of the alloy is reduced, increasing the possibility of alloy mixing. Depending on the composition, transition peaks appear at finite temperatures where there are changes of preferable atomic arrangement due to the effect of temperature via the configurational entropy. In addition, the analyses on short-range order parameter and bond fraction show that PdRu alloy prefers to be in segregated form, where Pd and Ru are immiscible at low temperature, consistent with the experimental observations. The random mixing of Pd and Ru atoms in the form of solid-solution can occur at high temperature.

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Assessing the Reactivity of the Na3PS4 Solid-State Electrolyte with the Sodium Metal Negative Electrode Using Total Trajectory Analysis with Neural-Network Potential Molecular Dynamics

Lieven Bekaert, Suzuno Akatsuka, Naoto Tanibata, Frank De Proft, Annick Hubin, Mesfin Haile Mamme, and Masanobu Nakayama

Rechargeable batteries play a central role in the global shift from fossil fuels to renewable energy. Since the commercial introduction of lithium-ion batteries in the early 1990s, recent progress is focused on the development of solid-state materials and new battery chemistries. Specifically, solid-state sodium ion batteries are an attractive alternative alongside lithium-based rechargeable batteries, with improvements in safety, lifespan, sustainability, and price. Critical to the battery performance are electrode–electrolyte interfaces since undesirable side-reactions often proceed between electrode and electrolyte materials. In addition, atomistic or electronic-level knowledge on the reactions at the interface is limited due to technical difficulties of experimental observation. Computational studies on interfacial reactivity, such as first-principles techniques, have also long been limited by computational limitations. Recent advances using neural-network potential molecular dynamics simulations are allowing significantly larger systems and longer timescales to be simulated at a significantly reduced computational cost without loss of accuracy. In this study, the chemical stability of the glass–ceramic Na3PS4 solid-state electrolyte with the sodium metal electrode is investigated through a combined total trajectory analysis computational and experimental approach. PS4 groups in the Na3PS4 material were found to decompose sequentially into PS3, PS2, PS, and phosphide and sulfide species through the insertion of sodium atoms. Whereas the decomposition is thermodynamically favored, it is kinetically hindered due to steric effects in the PS3 intermediate. Machine learning-assisted analysis was found to be able to visualize the reactivity tendencies of individual element types. The formed SEI layer exhibited a good chemical stability and a low electronic conductivity. These findings provide new design principles to optimize and develop new solid-state electrolytes with an increased chemical stability toward the sodium metal electrode.

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Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion

Hiroshi Sampei, Koki Saegusa, Kenshin Chishima, Takuma Higo, Shu Tanaka, Yoshihiro Yayama, Makoto Nakamura, Koichi Kimura, and Yasushi Sekine

Quantum annealing has been used to predict molecular adsorption on solid surfaces. Evaluation of adsorption, which takes place in all solid surface reactions, is a crucially important subject for study in various fields. However, predicting the most stable coordination by theoretical calculations is challenging for multimolecular adsorption because there are numerous candidates. This report presents a novel method for quick adsorption coordination searches using the quantum annealing principle without combinatorial explosion. This method exhibited much faster search and more stable molecular arrangement findings than conventional methods did, particularly in a high coverage region. We were able to complete a configurational prediction of the adsorption of 16 molecules in 2286 s (including 2154 s for preparation, only required once), whereas previously it has taken 38 601 s. This approach accelerates the tuning of adsorption behavior, especially in composite materials and large-scale modeling, which possess more combinations of molecular configurations.

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Effect of HFO Refrigerants on Lubrication Characteristics (Part 2) -Adsorption Characteristics of Various Refrigerants on Nascent Iron Surfaces and Molecular Simulation Analysis-

Yuji Shitara, Tasuku Onodera, Shigeyuki Mori

Following the previous paper focusing on tribological properties of HFO (hydro fluoro olefin) refrigerant, the adsorption behavior of refrigerants on the nascent iron surface was investigated experimentally and the adsorption structure, adsorption energy and dynamic process of chemical reaction of refrigerants were analyzed by a molecular simulation. The adsorption behavior on the nascent iron surface was highly dependent on the molecular structure of the refrigerant. HFO refrigerants with an unsaturated bond exhibited high adsorption activity, and halogen species also affected the adsorption activity. HFO showed higher adsorption activity than organic ester, phosphate ester and alkyl sulfide as model compounds of refrigerator oil. In adsorption simulation by neural network potential (NNP), HFO molecules showed large negative adsorption energy. To understand the mechanism for this stronger adsorption of HFO species, density functional theory calculation was conducted, and it showed that HFO adsorbs on iron surface by electron donation from the molecule and back-donation from iron surface. There was also a good correlation between the experimental adsorption activity and the NNP-obtained adsorption energy. MD simulation of molecule adsorbed on the nascent surface at temperature of 298 K was subsequently done using NNP technique. The results showed that CF₃CF = CH₂ (R1234yf) exhibits a distinct decomposition reaction with releasing F atoms and it generates several Fe-F bonds, meaning that precursor of iron fluorides forms on the surface. It is worthy to mention that a formation of the iron fluoride has been experimentally detected on friction track by using XPS in the first report of this study. It was concluded that the adsorption and tribochemical formation of iron fluoride from HFO are supported by molecular simulation performed in this study.

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Effect of HFO Refrigerants on Lubrication Characteristics (Part 1) -Tribological Characteristics under Refrigerant Atmosphere and Adsorption Characteristics on Nascent Metal Surface-

Yuji Shitara, Shigeyuki Mori

Focusing on HFO (Hydro Fluoro Olefin) refrigerant, which is expected to be applied as a green refrigerant, the effect of R1234yf on the tribological characteristics was compared with HFC (Hydro Fluoro Carbon) refrigerant R32. Tribological tests were carried out with and without a model lubricant containing triethyl phosphate as an EP additive. R1234yf showed a lower friction coefficient and better wear resistance than R32. XPS analysis of lubricated tracks revealed that metal fluoride was formed from R1234yf and R32 and higher amount of metal fluoride was observed on the track formed under R1234yf than that under R32. The frictional characteristics of R1234yf were affected by refrigerant pressure and temperature of specimen. The higher the pressure of refrigerant and the temperature of the specimen, the better the tribological characteristics were observed. In order to clarify adsorption of refrigerant on metal surface, scratching tests were carried out under refrigerant atmosphere using a mass spectrometer. Although R32 did not adsorb, R1234yf adsorbed on nascent steel surface formed by scratching. The adsorption rate of R1234yf increased linearly with scratching speed. Since propylene also adsorbed on nascent steel surface, π-electrons in R1234yf play an important role on the adsorption. It can be concluded that R1234yf adsorbs easily on nascent steel surface resulting in formation of iron fluoride which acts as a boundary lubrication layer.

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Innovation in Molecular Simulation Technologies for Tribology Using Artificial Intelligence

Tasuku Onodera

In this article, a newly developed universal neural network potential (NNP), enabling accelerated molecular simulations, and its application to tribological phenomena were reported. The NNP is based on a graph neural network theory and an original dataset including a huge number of the first-principles calculation results. Two application examples using this method were introduced. One was exploring geometry for a fundamental molecular adsorption of long-chain fatty acid, which is difficult to deal with the conventional first-principles calculations. This new technique successfully observed that coverage of fatty acids on metal surface largely influence on the adsorption structures, i.e., orientation angle and intermolecular interaction of long-chain alkyl group. The second application was a molecular dynamics simulation for understanding influence of base oil structures on adsorption behavior of an oiliness additive. The adsorption time of an oiliness additive in three types of base oil (normal paraffin, isoparaffin, and naphthene) was measured. It was found that an oiliness additive in isoparaffin phase showed the earliest adsorption time among the tested base oils. This study unveiled the adsorption mechanisms of the oiliness additive in the base oils: an oiliness additive can smoothly pass inside the isoparaffin-derived oil film with sparse structure and easily reaches metal surface. We concluded that a rapid tribo-material discovery, as well as analysis on molecular mechanism of tribological phenomena, is expected to be achieved by adapting universal NNP to molecular simulations.

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Comparison of Matlantis and VASP bulk formation and surface energies in metal hydrides, carbides, nitrides, oxides, and sulfides

Shinya Mine, Takashi Toyao, Ken-ichi Shimizu, and Yoyo Hinuma

Generic neural network potentials without forcing users to train potentials could result in significantly acceleration of total energy calculations. Takamoto et al. [Nat. Commun. (2022), 13, 2991] developed such a deep neural network potential (NNP) and made it available in their Matlantis package. We compared the Matlantis bulk formation, surface, and surface O vacancy formation energies of metal hydrides, carbides, nitrides, oxides, and sulfides with our previously calculated VASP values obtained from first-principles with the PBEsol(+U) functional. Matlantis bulk formation energies were consistently ~0.1 eV/atom larger and the surface energies were typically ~10 meV/Å2 smaller than the VASP counterpart. Surface O vacancy formation energies were generally underestimated within ~0.8 eV. These results suggest that Matlantis energies could serve as a relatively good descriptor of the VASP bulk formation and surface energies.

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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.

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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.

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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.

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