Preferred Computational Chemistry Celebrates Second Anniversary
Preferred ComPreferred Computational Chemistry (PFCC) marked its second anniversary on July 1, 2023. Thanks to Matlantis’s valued clients and all parties concerned, the Matlantis™ business has steadily grown with an increasing number of academic papers and case studies published by them.
Below are the two-year anniversary message from PFCC CEO Daisuke Okanohara and the latest list of academic papers and case studies published by Matlantis clients.
Message from Daisuke Okanohara, CEO:
“As PFCC celebrates its second year anniversary, I’d like to take this opportunity to express my sincerest gratitude to our clients and all those who have worked with us to achieve this milestone.
The past two years have been full of new challenges but thanks to everyone’s support and collaboration, our business has grown significantly. The client base for Matlantis has expanded to over 60 companies and academic institutions, and we could officially launch the service in the US in April 2023. Going forward, we will continue on our mission to support discovery of innovative materials for a sustainable future by working diligently to meet client needs.
As we renew our commitment on our two-year anniversary, we would like to thank all of our clients for their continued support, and we look forward to evolving further towards a sustainable future.”
Our Clients’ Research Papers related to Matlantis
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
Cyber Catalysis: N2 Dissociation over Ruthenium Catalyst with Strong Metal-Support Interaction
Gerardo Valadez Huerta, Kaoru Hisama, Katsutoshi Sato, Katsutoshi Nagaoka, Michihisa Koyama
Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential
Kaoru Hisama, Gerardo Valadez Huerta, Michihisa Koyama
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, Yoyo Hinuma
Innovation in Molecular Simulation Technologies for Tribology Using Artificial Intelligence
Tasuku Onodera
Effect of HFO Refrigerants on Lubrication Characteristics (Part 1)
Yuji SHITARA, Shigeyuki MORI
Effect of HFO Refrigerants on Lubrication Characteristics (Part 2)
Yuji SHITARA, Tasuku ONODERA, Shigeyuki MORI
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
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
On the Thermodynamic Stability of Alloys: Combination of Neural Network Potential and Wang-Landau Sampling
Tien Quang Nguyen, Yusuke Nanba, and Michihisa Koyama
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
CO Adsorption on Ternary Nanoalloys by Universal Neural Network Potential
Ayako TAMURA, Gerardo VALADEZ HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA
