HomeNewsMatlantis Public Information List
Others

Matlantis Public Information List

Research papers on Matlantis List

TitleAuthorYearCitation
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elementsSo Takamoto, et al. 2022Nature Communications,
13, 2991
Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential Kaoru Hisama,
et al.
2022Computational Materials Science, 218, 111955
Innovation in Molecular Simulation Technologies for Tribology Using Artificial Intelligence Tasuku Onodera 2022Tribologist, 67, 821-829
Effect of HFO Refrigerants on Lubrication Characteristics (Part 1) -Tribological Characteristics under Refrigerant Atmosphere and Adsorption Characteristics on Nascent Metal Surface- Yuji Shitara and Shigeyuki Mori 2022Tribologist, 67, 662-671
Effect of HFO Refrigerants on Lubrication Characteristics (Part 2) -Adsorption Characteristics of Various Refrigerants on Nascent Iron Surfaces and Molecular Simulation Analysis- Yuji Shitara,
et al.
2023Tribologist, 68, 280-291
Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion Hiroshi Sampei,
et al.
2023JACS Au, 3, 991–996
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,
et al.
2023J. Phys. Chem. C, 18, 8503–8514
On the Thermodynamic Stability of Alloys: Combination of
Neural Network Potential and Wang-Landau Sampling
Tien Quang Nguyen, et al. 2023J. Comput. Chem. Jpn., 21, 111–117
Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and ‘Ugly Duckling’ Dilemma Kan Hatakeyama, et al. 2023
CO Adsorption on Ternary Nanoalloys by Universal Neural Network PotentialAyako TAMURA,
et al.
2023J. Comput. Chem. Jpn., 21, 129-133
Increasing the Sodium Metal Electrode Compatibility with the Na3PS4 Solid-State Electrolyte through Heteroatom Substitution Lieven Bekaert,
et al.
2023ChemSusChem2023, e202300676

Conference Presentation List

TitleDateSpeakerLinks
Development of Innovative Molecular Simulation Method Based on Machine Learning and Its Application to Tribology ResearchOctober,
2021
ENEOS,
Tasuku Onodera et al.
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htm
Network complexes with selective CO2 storage capacity using pore spaceNovember,
2021
Tokyo Institute of Technology, Kosuke Shimada
et al.
Journal of the Crystallographic Society of Japan
https://crsj.jp/en/
Theoretical Investigation of N2 Adsorption on Supported Ru Nanoparticles on Partially Reduced La0.5Ce0.5O1.75 by Neural Network Potential CalculationsDecember,
2021
Shinshu University, Gerardo Valadez Huerta et al. Annual Meeting of MRS-J
https://www.mrs-j.org/meeting2021/en/
【Invited Speech】
Case studies of Matlantis in ENEOS corporation
December,
2021
ENEOS,
Takeshi Ibuka
QPARC
https://www.qparc.qunasys.com/?lang=en
【Keynote Speech】
Rapid Screening of Catalytic Reactivity based on Universal Neural Netowrk Potential
March,
2022
Shinshu University,
Michihisa Koyama
Chemical Catalyst 2022
https://catalysisconference.mindauthors.com/march-2022/
Computer Automated Material Design by Universal Neural Network PotentialMarch,
2022
Shinshu University, Gerardo Valadez Huerta et al. The Society of Chemical Engineers, Japan
http://www3.scej.org/meeting/87a/en_index.html
【Invited Speech】
Molecular Simulation for Tribology Research: Technological Breakthrough and Application
March,
2022
ENEOS,
Tasuku Onodera
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htm
NNP technology and Fugaku collaboration for Perovskite properties calculation and materials explorationMarch,
2022
ENEOS,
Hiroki Kotaka
RIKEN Center for Computational Science
https://www.r-ccs.riken.jp/en/
【Keynote Speech】
The Challenge of Low-Carbon Technology at ENEOS: Co-Developers and Users’ Perspectives on Matlantis™
March,
2022
ENEOS,
Takeshi Ibuka
The Chemical Society of Japan
https://confit.atlas.jp/guide/event/csj102nd/top

Slide
https://www.slideshare.net/Matlantis/eneosmatlantis
Pore structure design for selective CO2 adsorptionMarch,
2022
Tokyo Institute of Technology, Kosuke Shimada
et al.
The Chemical Society of Japan
https://confit.atlas.jp/guide/event/csj102nd/sessions/classlist/AP09B
【Invited Speech】
Catalyst and Materials Science in Digital Era – Leaders’ Competencies and Paradigm
May,
2022
Shinshu University,
Michihisa Koyama
Seminar at Advanced Ceramics Research Center, Nagoya Unviersity
https://www.nitech.ac.jp/eng/research/centers/index.html
Influence of base oil structures on adsorption properties of oiliness additives in metal forming fluidsMay,
2022
ENEOS, Junya Yamagishi
et al.
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htm
Development of a Molecular Dynamics Method under Electric Field Based on Neural Network PotentialJune,
2022
Shinshu University, Kaoru Hisama et al. Society of Computer Chemistry, Japan
https://sites.google.com/view/sccj2022autumn/program
Catalysts Studies with Universal Neural Network PotentialJuly,
2022
Shinshu University, Gerardo Valadez Huerta et al. TOCAT9
https://tocat.catsj.jp/9/
The challenge of Materials Informatics at ENEOS: co-developers and users’ perspectives on Matlantis™July,
2022
ENEOS,
Hideki Ono
LabTech Talk
https://labaseplus-event220701.peatix.com/?lang=zh-tw

Slide
https://speakerdeck.com/sangthae/eneosniokerumateriaruzuinhuomateikusuhefalsequ-zu-mi-fan-yong-yuan-zi-reberusimiyureta-matlantisfalsegong-tong-kai-fa-zhe-はositeyuzafalseshi-dian-kara-polgong-cui-semina-20220701
Catalyst screening method using a universal neural network potentialJuly,
2022
ENEOS,
Yoshihiro Yayama et al.
TOCAT9
https://tocat.catsj.jp/9/
【Invited Speech】 Activities of Data-driven AI Laboratory and Perspectives on Cyber CatalysisSeptember,
2022
Shinshu University,
Michihisa Koyama
The Society of Chemical Engineers, Japan
http://www3.scej.org/meeting/53f/prog/en_session_ST-21.html
【Invited Speech】
An Approach to Materials Informatics by Integrating Simulation and Machine Learning: Perspectives on the Use of Innovative Simulators
September,
2022
ENEOS,
Yuya Nakajima
et al.
Symposium on Macromolecules
https://main.spsj.or.jp/tohron/71tohron/en/
Catalytic Properties of N2 on Ru/La0.5Ce0.5O1.75-x revealed by a Universal Neural Network
Potential
November,
2022
Shinshu University, Kaoru Hisama et al. Society of Computer Chemistry, Japan
https://sites.google.com/view/sccj2022autumn/
On the Thermodynamic Stability of Alloys: Combination of Neural Network Potential and
Wang-Landau Sampling
November,
2022
Shinshu University,
Tien Quang Nguyen et al.
Society of Computer Chemistry, Japan
https://sites.google.com/view/sccj2022autumn/
Frictional properties of polymer in the aspect of the adsorption characteristics of lubricant additives ~Analysis with novel AI simulator~November,
2022
ENEOS,
Rui Ogata
et al.
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htmm
Analysis of the Behavior of Extreme Pressure Additives on Metal Surfaces by Molecular Dynamics Using Neural Network Potential November,
2022
University of Hyogo
Tomohito Horio
et al.
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htmm
Influence of Base Oil Composition on Metalworking Fluid Performance November,
2022
ENEOS, Junichi Shibata
et al.
Japanese Society of Tribologists
https://www.tribology.jp/indexe.htmm
Development of a Screening Scheme for Exploring Li-ion Battery Cathode Materials: Combination of Neural Network Potential and Wang-Landau SamplingDecember,
2022
Shinshu University,
Tien Quang Nguyen et al.
32nd Annual Meeting of MRS-J
https://www.mrs-j.org/meeting2022/en/
Development of universal machine learning potential and the application to elucidation of catalytic mechanisms and materials explorationDecember,
2022
ENEOS,
Yoshihiro Yayama and Tasuku Onodera
Catalysis Society of Japan
https://www.simulation.imr.tohoku.ac.jp/CACatal/index.html
【Invited Speech】
Molecular simulation technology and its application to lubricants research
December,
2022
ENEOS,
Tasuku Onodera
The Japan Petroleum Institute
https://www.sekiyu-gakkai.or.jp/en/index.html