Publications

Research Areas :

Chemoinformatics / Materials Science

Efficient Universal Potential Distillation with Pre-trained Students in LightPFP

NeurIPS2025 AI4Mat

Chemoinformatics / Materials Science

By : Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang, Yuta Tsuboi, Ryuhei Okuno, So Takamoto

P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials

NeurIPS2025 ML4PS

Chemoinformatics / Materials Science

By : Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi, Yong-Bin Zhuang, Wenwen Li

A practical guide to machine learning interatomic potentials – Status and future

Current Opinion in Solid State and Materials Science

Chemoinformatics / Materials Science

By : Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, , Kamal Choudhary, Gabor Csanyi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, KJ Schmidt, So Takamoto, Aidan P. Thompson, Julia Westermayr, Brandon M. Wood

Dielectric tensor prediction for inorganic materials using latent information from preferred potential

npj Computational Materials

Chemoinformatics / Materials Science

By : Zetian Mao, Wenwen Li, Jethro Tan

High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge

Scientific Reports

Chemoinformatics / Materials Science

By : Ryohto Sawada, Kosuke Nakago, Chikashi Shinagawa, So Takamoto

Towards universal neural network interatomic potential

Journal of Materiomics

Chemoinformatics / Materials Science

By : So Takamoto, Daisuke Okanohara, Qing-Jie Li, Ju Li

Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Nature Communications, 2022

Chemoinformatics / Materials Science

By : So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada, Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide Hayashi, Nontawat Charoenphakdee, Takeshi Ibuka

Population-based De Novo Molecule Generation, Using Grammatical Evolution

Chemistry Letters, 47, 1431-1434, 2018

Chemoinformatics / Materials Science

By : Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, Koji Tsuda

Semi-supervised learning of hierarchical representations of molecules with neural message passing

Machine Learning for Molecules and Materials in NIPS 2017

Chemoinformatics / Materials Science

By : Hai Nguyen, Shin-ichi Maeda, Kenta Oono