5 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…
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…