論文・学会発表

PFN所属研究者、インターン生やパートタイム・エンジニア学生が著者に含まれる国内外会議・ジャーナル発表論文を掲載しています。共同研究による成果も含みます。

NeurIPS2025 AI4Mat

Efficient Universal Potential Distillation with Pre-trained Students in LightPFP

材料科学／計算化学

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

NeurIPS2025 ML4PS

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

材料科学／計算化学

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

Current Opinion in Solid State and Materials Science

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

材料科学／計算化学

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

npj Computational Materials

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

材料科学／計算化学

By : Zetian Mao, Wenwen Li, Jethro Tan

Scientific Reports

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

材料科学／計算化学

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

Journal of Materiomics

Towards universal neural network interatomic potential

材料科学／計算化学

By : So Takamoto, Daisuke Okanohara, Qing-Jie Li, Yuta Tsuboi

論文・学会発表

Efficient Universal Potential Distillation with Pre-trained Students in LightPFP

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

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

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

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

Towards universal neural network interatomic potential

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未掲載事例、プロダクト・ソリューション、研究開発についてお気軽にお問い合わせください

論文・学会発表

Efficient Universal Potential Distillation with Pre-trained Students in LightPFP

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

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

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

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

Towards universal neural network interatomic potential

PFNは新しい仲間を募集しています

PFNは新しい仲間を募集しています

PFNは新しい仲間を
募集しています

PFNは新しい仲間を
募集しています