38 citations · 79 across the 13 of their papers we have counts for
13 papers
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen +11
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantial…
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…
ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
Yuji Tone, Masatoshi Hanai, Mitsuaki Kawamura +2
The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this…
ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials Science
Masatoshi Hanai, Ryo Ishikawa, Mitsuaki Kawamura +16
In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge…
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Xiaohang Xu, Toyotaro Suzumura, Jiawei Yong +5
Next Point-of-Interest (POI) recommendation plays a crucial role in urban mobility applications. Recently, POI recommendation models based on Graph Neural Networks (GNN) have been…
On Data Imbalance in Molecular Property Prediction with Pre-training
Limin Wang, Masatoshi Hanai, Toyotaro Suzumura +2
Revealing and analyzing the various properties of materials is an essential and critical issue in the development of materials, including batteries, semiconductors, catalysts, and…
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?
Shun Takashige, Masatoshi Hanai, Toyotaro Suzumura +2
The prediction of material properties plays a crucial role in the development and discovery of materials in diverse applications, such as batteries, semiconductors, catalysts, and…
BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
Hayato Shiba, Masatoshi Hanai, Toyotaro Suzumura +1
Recent developments in machine learning have enabled accurate predictions of the dynamics of slow structural relaxation in glass-forming systems. However, existing machine-learning…
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa +30
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability…
Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling
Masatoshi Hanai, Nikos Tziritas, Toyotaro Suzumura +2
The dynamic scaling of distributed computations plays an important role in the utilization of elastic computational resources, such as the cloud. It enables the provisioning and de…
Global Data Science Project for COVID-19
Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao +14
This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both dire…
Performance Evaluation of Dynamic Scaling on MPI
Masatoshi Hanai, Georgios Theodoropoulos
Dynamic scaling aims to elastically change the number of processes during runtime to tune the performance of the distributed applications. This report briefly presents a performanc…
Distributed Edge Partitioning for Trillion-edge Graphs
Masatoshi Hanai, Toyotaro Suzumura, Wen Jun Tan +3
We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partiti…