Authors: Ryo Yoshida | Yasuhiko Hayashi | Hidemine Furuya | Ryohei Hosoya | Keiichi Kaneko | Hiroki Sugisawa | Yu KANEKO | Aiko Takahashi | Yoh Noguchi | Shun Nanjo | Keiko Shinoda | Tomu Hamakawa | Mitsuru Ohno | Takuya Kitamura | M. Yonekawa | Stephen Wu | Masato Ohnishi | Chang Liu | Teruki Tsurimoto | Arifin Arifin | Araki Wakiuchi | Kohei Noda | Junko Morikawa | Teruaki Hayakawa | Junichiro Shiomi | Masanobu Naito | Kazuya Shiratori | Tomoyuki Nagai | Norio Tomotsu | Hiroto Inoue | Reiko Sakashita | Masashi Ishii | Isao Kuwajima | Kenji Furuichi | Naoki Hiroi | Yuki Takemoto | Takahiro Ohkuma | Kazuhiro Yamamoto | Naoya Kowatari | Masato Suzuki | Naoya Matsumoto | Shunji Umetani | Hisaki Ikebata | Yasuhiro Shudo | Mayu Nagao | Shinya KAMADA | Koichiro Kamio | TAKASHI SHOMURA | Kensaku Nakamura | Yudai Iwamizu | Akihiro Abe | K. Yoshitomi | Yuki Horie | Katsuhiko Koike | Koichi Iwakabe | Shinya Gima | Keisuke Usui | Gikyo Usuki | Takuro Tsutsumi | K. Matsuoka | Kazuki Sada | Miki Kitabata | Takuma Kikutsuji | Akitaka Kamauchi | Yusuke Iijima | Tsubasa Suzuki | Thomas Goda | Yuki Takabayashi | Kohsuke Imai | Yuji Mochizuki | Hideo Doi | Koji Okuwaki | Hiroya Nitta | Taku Ozawa | Hitoshi Kamijima | Takafumi Shintani | Tadayuki Mitamura | Massimiliano Zamengo | Yuitsu Sugami | Seiji Akiyama | Yukio Murakami | Atsushi Betto | Naoya Matsuo | Satoru Kagao | Tetsuya Kobayashi | N. Matsubara | Shosei Kubo | Yuki Ishiyama | Yoshiki Ichioka | Mamoru Usami | Sayaka Yoshizaki | Shuki Mizutani | Y. Hanawa | Shogo Kunieda | Makoto YAMBE | Takeru Nakamura | Hiromori Murashima | Kenji Takahashi | Naoki Wada | M Kawano
Source: ArXiv.org (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Omics-scale polymer computational database transferable to real-world artificial intelligence applications Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science. Bridge (graph theory) ; Property (philosophy) ; Computational model ; Artificial life ; Benchmarking ; Range (aeronautics) ; Applications of artificial intelligence ; Milestone Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions