2nd Anniversary of Preferred Computational Chemistry

Preferred Computational Chemistry (PFCC) celebrated its second anniversary of business on July 1, 2023.
We would like to express our sincere gratitude to our customers and all other parties involved.

Over the past year (the second year since the start of the PFCC project), the PFCC/Matlantis project has made steady progress, and the number of customer use cases for Matlantis (publication of papers, etc.) has increased. Therefore, in this press release, we will include a "Greetings from President and CEO Okanohara" and a "Summary of Matlantis Use Cases."

Greetings from President and CEO Okanohara

We are now celebrating our second anniversary since the start of our business. We would like to express our sincere gratitude to everyone.

Over the past two years, we have been able to achieve steady growth while facing various new challenges thanks to your support and cooperation. Specifically, over the past two years, more than 60 companies and academic institutions have used Matlantis ^TM, and in April 2023, we were able to fully launch our business in the United States. We will continue to sincerely address the challenges and needs of our customers and strive to achieve our mission of "contributing to the creation of innovative materials and realizing a sustainable world."

As we celebrate our second anniversary since the start of our business, we are determined to evolve even further and take a new step forward. We sincerely ask for your continued support and cooperation.

Preferred Computational Chemistry, Inc.
Representative Director and President Daisuke Okanohara

Summary of Matlantis use cases

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

In this paper, we use Matlantis to calculate NO adsorption on single-metal Ru nanoparticles, PdRuCu ternary alloy nanoparticles, and Rh nanoparticles, and compare and verify the results with first-principles calculations. This is a useful example for those who want to calculate multi-element alloys.

Cyber Catalysis: N 2 Dissociation over Ruthenium Catalyst with Strong Metal-Support Interaction

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

In this paper, we use Matlantis to calculate various adsorption structures for a complex catalyst model, a Ru-supported nanoparticle, and clarify the reaction mechanism and maximum activity conditions. This is a useful example for those who want to develop heterogeneous catalysts using simulations.

*For more information about this paper, please also refer to the presentation materials and videos here.

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

Kaoru Hisama, Gerardo Valadez Huerta, Michihisa Koyama

In this paper, the electrical conductivity of oxide ions in yttria-stabilized zirconia and protons in hydrochloric acid solution is calculated using Matlantis. This is a useful example for those who want to perform electrical conductivity calculations under an external electric field.

*For more information about this paper, please also refer to the presentation materials and videos here.

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

Shinya Mine, Takashi Toyao, Kenichi Shimizu, Yoyo Hinuma

In this paper, we perform benchmark calculations (comparison of Matlantis calculation values with VASP calculation values) of bulk formation energy, surface energy, and defect generation energy for various metal compounds. This is a useful example for those who are interested in the Matlantis benchmark results.

Innovation in Molecular Simulation Technologies for Tribology Using Artificial Intelligence

Tasuku Onodera

In this paper, the adsorption behavior of long-chain fatty acids on metal surfaces is simulated using Matlantis. This is a useful example for those who want to clarify the mechanisms of tribological phenomena.

Effect of HFO Refrigerants on Lubrication Characteristics (Part 1)

Yuji SHITARA, Shigeyuki MORI

In this paper, the adsorption energy of HFO (Hydro Fluoro Olefin) refrigerant on metal surfaces is calculated using Matlantis. This is a useful example for those who want to clarify the mechanism of tribological phenomena.

Effect of HFO Refrigerants on Lubrication Characteristics (Part 2)

Yuji SHITARA, Tasuku ONODERA, Shigeyuki MORI

In this paper, the adsorption and reaction of HFO (Hydro Fluoro Olefin) refrigerant on a nascent iron surface is simulated using Matlantis. This is a useful example for those who want to clarify the mechanism of tribological phenomena.

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

In this paper, we use a quantum computer (annealing method) to predict various molecular adsorption patterns, and compare the predictions with the Matlantis calculation results to verify their accuracy. This is an example of Matlantis being used as an evaluation standard for verification.

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

In this paper, we performed molecular dynamics calculations using Matlantis on an electrode-electrolyte interface model of a sodium-ion battery, and compared the boundary film formation behavior with observations and experimental facts. This is a useful example for those who want to perform simulations of the electrode-electrolyte interface of battery materials.

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

Tien Quang Nguyen, Yusuke Nanba, and Michihisa Koyama

In this paper, we combine the Preferred Potential (Matlantis potential) and Monte Carlo methods to explore the thermodynamic properties and atomic configuration of PdRu alloys. This is a useful example for those who want to calculate the thermodynamic properties of alloys.

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

In this paper, the Matlantis calculation results are used as explanatory variables in the task of predicting the refractive index of polymers using GPT-4. In particular, this is a useful example for those who want to build a property prediction model using the Matlantis calculation results as descriptors.

*For more information about this paper, please also refer to the presentation materials and videos here.

CO Adsorption on Ternary Nanoalloys by Universal Neural Network Potential

Ayako Tamura, Gerardo Valadez Huerta, Yusuke Nanba, Kaoru Hisama, Michihisa Koyama

In this paper, we develop a method to automatically calculate hundreds of adsorption sites using PFP for the catalytic properties of multi-component alloy nanoparticles. This is a useful example for those who wish to develop catalysts using simulations.