Presentation and Exhibition at 2024 MRS Fall Meeting & Exhibit

PFCC will be presenting and exhibiting at 2024 MRS Fall Meeting & Exhibit held December 1 – 6, 2024. PFCC will present three posters. In addition, there will be three poster presentations and seven oral presentations from external collaborators. We invite you to explore our latest research and findings. Please see below for details of the exhibit.

Exhibition and booth information

Expo hoursTuesday, December 3 – Thursrday, December 5, 2024
Venue: Hynes Convention Center, Boston, Massachusetts
Booth: 419

Presentation Information

Symposium: MT04: Next-Generation AI-Catalyzed Scientific Workflow for Digital Materials Discovery
Room: Hynes, Level 1, Hall A

Presenter 1: Taku Watanabe

Session:MT04.05.11
Session Date and Time:2December 3, 2024 from 8:00 PM to 10:00 PM EST
Session TypePoster Presentation
Title: The Surface Configurations and Their Impact on Pd-Based Alloy Membranes for Hydrogen Separation—An Application of a Universal Neural Network Potential
Abstract:Pd-based alloy membranes for hydrogen separation offer high efficiency and important practical advantages over traditional methods like pressure swing adsorption or cryogenic distillation. However, the rarity and expense of Pd, along with its reactivity with certain gas mixture components, pose significant challenges. Our study uses the Preferred Potential (PFP) neural network to perform atomistic simulations that explore the impact of alloy configurations on hydrogen adsorption and diffusion at the surface. PFP allows near-DFT accuracy in simulating chemical reactions on larger systems and at higher speeds.
Our findings using Monte Carlo simulations indicate pronounced surface segregation behaviors in fcc Pd-based alloys, with specific patterns for Pd3Ag, Pd3Au, Pd3Cu, Pd3Ni, and Pd3Pt. These results align with known literature. Additionally, ternary phases like Au-Ag, Au-Cu, and Cu-Ag in Pd reveal similar segregation trends and interesting subsurface structures. The variation in surface composition significantly affects hydrogen binding energies and migration energy barriers, suggesting that the choice of alloy composition could optimize membrane performance for hydrogen separation.

Takashi Watanabe
After receiving his Ph.D. in Materials Science and Engineering from the University of Florida, he worked as a research associate in chemical engineering at the Georgia Institute of Technology.
He joined the Samsung Japan Research Institute in 2012 and worked on research into solid-state batteries for approximately eight years.
His current research interests include battery materials, nanoporous solids, surface science, and the application of machine learning techniques in computational chemistry.

Presenter 2: Akihiro Nagoya

Session:MT04.05.17
Session Date and Time:December 3, 2024 from 8:00 PM to 10:00 PM EST
Session Type: Poster Presentation
Title:Evaluation of a Universal Neural Network Potential for Predicting Finite Temperature Properties Using Quasi-Harmonic Approximation
Abstract:Predicting material properties at finite temperature requires accurate evaluation of thermodynamic quantities such as Gibbs free energy. The Preferred Potential (PFP) implemented on Matlantis^TMis a recently developed graph neural network potential with the unique feature of universality[1]. PFP is trained on large DFT data sets, including not only stable crystals and molecules, but also surfaces and disordered structures. As a result, it is applicable to predict finite temperature properties of materials without compromising accuracy.
In this study, we have systematically validated the accuracy of PFP for predicting thermodynamics properties at finite temperature. The temperature dependence of the specific heats around room temperature shows good agreement with experiments. The miscibility gap of MgO-CaO was qualitatively predicted using semi-Grand Canonical Monte Carlo (sGCMC) simulations. These results demonstrate the accuracy and universality of PFP which is applicable to predict material properties at finite temperature.

Akihiro Nagoya
Senior Customer Success Engineer at PFCC. He graduated from Osaka University in 2004. Then, he worked as a researcher at Toyota Central R&D Labs, Inc. For about 15 years, he was involved in research on solar cell materials, fuel cell catalysts using first-principles calculations for two-dimensional materials and materials informatics for polymers using classical MD calculations. His current interest is in applications of machine-learning potential to battery materials and metallic materials.

Presenter 3: Kota Matsumoto

Session:MT04.09.10
Session Date and Time:December 4, 2024 from 8:00 PM to 10:00 PM EST
Session Type:Poster Presentation
Title:Wet Hydrofluoric Acid Etching Reaction Mechanism Analysis of Silicon Oxide Using GRRM with Universal Neural Network Potential
Abstract:In semiconductor manufacturing technology that is advancing towards miniaturization, the improvement of wet etching process reactions at the atomic level, which utilizes dilute hydrofluoric (HF) acid solution to remove oxides from substrate surfaces, has become increasingly important. Although there has been active research aimed at understanding at the atomic level, it has been difficult to handle the formation and cleavage of bonds in molecular dynamics simulations, and due to the high computational cost, first-principle calculations have struggled with the analysis of extensive phenomena.
In this study, we report the results of our attempt at a comprehensive reaction analysis of wet etching, combining PFP implemented in Matlantis^TMand the SC-AFIR method implemented in GRRM20, which enable us to explore complex, multi-step reactions involving multiple molecules. We report the comparison results of reaction pathways considering the stabilization of intermediates by surrounding HF and H2O in the four-step fluorination reaction of Si.

Kota Matsumoto
Customer Success Engineer at PFCC. He completed the master’s program at the Graduate School of Chemical Sciences and Engineering, Hokkaido University, and joined ENEOS Corporation. There, he engaged in research on materials development using computational chemistry, the validation and development of Matlantis, and the interface development for GRRM20 with Matlantis. Subsequently, he joined PFCC, where he currently focuses on application development using Matlantis.

External Collaborators’ Presentation

December 3, 2024

Session:MT04.05.19
Session Date and Time:December 3, 2024 from 8:00 PM to 10:00 PM EST
Room: Hynes, Level 1, Hall A
Session Time:Poster Presentation
Title:High-Throughput Computational Search for Stable Compositions and Configurations in High-Entropy Perovskite SrTiO3
Presenters:Hiroki Kotaka, Yosuke Harashima, Hiroki Iriguchi, Tomoaki Takayama, Shogo Takasuka, Mikiya Fujii
Organization:ENEOS Corporation & Nara Institute of Science and Technology

December 4, 2024

Session:MT04.06.10
Session Date and Time:December 4, 2024, 11:00 AM EST
Room: Hynes, Level 2, Room 210
Session Type:Oral Presentation
Title:Multi-Objective Bayesian Optimization for Materials Discovery with Neural Network Potential—An Application to Li-Ion Battery Cathode Material
Presenters:Shuhei Watanabe, Hideaki Imamura, Chikashi Shinagawa, Kohei Shinohara, So Takamoto, Ju Li
Organizaion:Preferred Networks, inc. & Massachusetts Institute of Technology

Session:MT04.09.08
Session Date and Time:December 4, 2024 from 8:00 PM to 10:00 PM EST
RoomHynes, Level 1, Hall A
Session TypePoster Presentation
Title:Accelerating Advanced Material Design Through Versatile Atomistic Scale AI Simulator MATLANTIS
Presenters:Yuji Hakozaki, Tasuku Onodera, Takashi Kojima
Organization:ENEOS Corporation

Session:MT04.09.11
Session Date and Time:December 4, 2024 from 8:00 PM to 10:00 PM EST
Room:Hynes, Level 1, Hall A
Session Type:Poster Presentation
Title:Investigation of Bayesian Optimization and Generative Model for Crystal Structure Prediction of Molecular Crystals
Presenters:Takuya Taniguchi
Organization:Waseda University

December 5, 2024

Session:MT04.10.01
Session Date and TimeDecember 5, 2024, 8:00 AM EST
Room:Hynes, Level 2, Room 210
Session Type:Oral Presentation
Title:Flow for Generating Reaction Pathways and Validation of the Trained Neural Network
Presenters:Akihide Hayashi, So Takamoto, Ju Li, Hirotaka Akita, Daisuka Okanohara
Organization:Preferred Networks, inc. & Massachusetts Institute of Technology

Session:MT04.11.03
Session Date and Time:December 5, 2024, 2:15 PM EST
Room:Hynes, Level 2, Room 210
Session Time:Oral Presentation
Title:Towards r2SCAN-level Universal Neural Network Potential for Materials Discovery
Presenter:Chikashi Shinagawa, So Takamoto, Daiki Shintani, Katsuhiko Nishimra, Ju Li
Organization:Preferred Networks, inc. & Massachusetts Institute of Technology

Session:MT04.11.04
Session Date and Time:December 5, 2024, 2:30 PM EST
Room:Hynes, Level 2, Room 210
Session Type:Oral Presentation
Title:Support for 96 Elements and Improved Robustness of Universal Neural Network Potential PFP
Presenters:So Takamoto, Chikashi Shinagawa, Daiki Shintani, Katsuhiko Nishimra, Ju Li
Organization:Preferred Networks, inc. & Massachusetts Institute of Technology

Sessoin:MT04.11.08
Session Date and TimeDecember 5, 2024, 4:15 PM EST
Room:Hynes, Level 2, Room 210
Session Type:Oral Presentation
Title:LightPFP—Accelerating the Development of Task-Specific Machine Learning Potentials Using Universal Potential
Presenters:Wenwen Li, Nontawat Charoenphakdee, Yuta Tsuboi, So Takamoto, Ju Li
Organization:Preferred Networks, inc. & Massachusetts Institute of Technology

December 6, 2024

Session:MT04.12.07
Session Date and Time:December 6, 2024, 9:30 AM EST
Room:Hynes, Level 2, Room 210
Session Type:Oral Presentation
Title:Accelerated Quantum Chemical Simulations for Oxygen Evolution Reaction Catalysts Using “PreFerred Potential” (PFP)—A Pathway to Efficient Material Design
Presenters:Hiroki Kotaka, Yuji Hakozaki, Terumasa Shimada, Yoichiro Kawami, Yoshitatsu Misu, Atsushi Fukazawa, Yusuke Hasegawa
Organization:ENEOS Corporation

Session:MT04.12.11
Session Date and Time:December 6, 2024, 11:00 AM EST
Room:Hynes, Level 2, Room 210
Session Time:Oral Presentation
Title:Finite-Temperature Crystal Structure Prediction with Universal Neural Network Potential and Free Energy Calculation
Presenters:Kohei Shinohara, Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra, Chikashi Shinagawa, So Takamoto, Ju Li
Organization:Preferred Networks, inc. & Massachusetts Institute of Technology

For more information about the conference (External site) >>2024 MRS Fall Meeting & Exhibit