Accelerating material design with the generative toolkit for scientific discovery

被引:19
|
作者
Manica, Matteo [1 ]
Born, Jannis [1 ]
Cadow, Joris [1 ]
Christofidellis, Dimitrios [1 ]
Dave, Ashish [2 ]
Clarke, Dean [2 ]
Teukam, Yves Gaetan Nana [1 ]
Giannone, Giorgio [1 ]
Hoffman, Samuel C. [3 ]
Buchan, Matthew [2 ]
Chenthamarakshan, Vijil [3 ]
Donovan, Timothy [2 ]
Hsu, Hsiang Han [4 ]
Zipoli, Federico [1 ]
Schilter, Oliver [1 ]
Kishimoto, Akihiro [4 ]
Hamada, Lisa [4 ]
Padhi, Inkit [3 ]
Wehden, Karl [3 ]
McHugh, Lauren [3 ]
Khrabrov, Alexy [5 ]
Das, Payel [3 ]
Takeda, Seiji [4 ]
Smith, John R. [3 ]
机构
[1] IBM Res Europe Zurich, Ruschlikon, Switzerland
[2] IBM Res UK, Hursley, England
[3] IBM Res Yorktown Hts, New York, NY USA
[4] IBM Res Tokyo, Tokyo, Japan
[5] IBM Res Almaden, San Jose, CA USA
关键词
DEEP; DDR1;
D O I
10.1038/s41524-023-01028-1
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
引用
收藏
页数:6
相关论文
共 50 条
  • [11] Accelerating scientific discovery through crowdsourced computing
    Hindo, Juan
    Pyzer-Knapp, Edward
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 252
  • [12] Accelerating scientific discovery through computation and visualization II
    Sims, JS
    George, WL
    Satterfield, SG
    Hung, HK
    Hagedorn, JG
    Ketcham, PM
    Griffin, TJ
    Hagstrom, SA
    Franiatte, JC
    Bryant, GW
    Jaskólski, W
    Martys, NS
    Bouldin, CE
    Simmons, V
    Nicolas, OP
    Warren, JA
    Ende, BAA
    Koontz, JE
    Filla, BJ
    Pourprix, VG
    Copley, SR
    Bohn, RB
    Peskin, AP
    Parker, YM
    Devaney, JE
    JOURNAL OF RESEARCH OF THE NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY, 2002, 107 (03): : 223 - 245
  • [13] Accelerating Scientific Discovery With AI-Aided Automation
    Schneider, Tapio
    Altintas, Ilkay
    Atkins, Daniel
    COMPUTING IN SCIENCE & ENGINEERING, 2023, 25 (05) : 27 - 30
  • [14] Accelerating discovery of bioactive ligands with pharmacophore-informed generative models
    Xie, Weixin
    Zhang, Jianhang
    Xie, Qin
    Gong, Chaojun
    Ren, Yuhao
    Xie, Jin
    Sun, Qi
    Xu, Youjun
    Lai, Luhua
    Pei, Jianfeng
    NATURE COMMUNICATIONS, 2025, 16 (01)
  • [15] Accelerating drug target inhibitor discovery with a deep generative foundation model
    Chenthamarakshan, Vijil
    Hoffman, Samuel C.
    Owen, C. David
    Lukacik, Petra
    Strain-Damerell, Claire
    Fearon, Daren
    Malla, Tika R.
    Tumber, Anthony
    Schofield, Christopher J.
    Duyvesteyn, Helen M. E.
    Dejnirattisai, Wanwisa
    Carrique, Loic
    Walter, Thomas S.
    Screaton, Gavin R.
    Matviiuk, Tetiana
    Mojsilovic, Aleksandra
    Crain, Jason
    Walsh, Martin A.
    Stuart, David I.
    Das, Payel
    SCIENCE ADVANCES, 2023, 9 (25):
  • [16] GPro: generative AI-empowered toolkit for promoter design
    Wang, Haochen
    Du, Qixiu
    Wang, Ye
    Xu, Hanwen
    Wei, Zheng
    Wang, Xiaowo
    BIOINFORMATICS, 2024, 40 (03)
  • [17] Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery
    Park, Donggeun
    Lee, Jaemin
    Park, Kundo
    Ryu, Seunghwa
    ADVANCED ENGINEERING MATERIALS, 2023, 25 (21)
  • [18] EMPRESS: Accelerating Scientific Discovery through Descriptive Metadata Management
    Lawson, Margaret
    Gropp, William
    Lofstead, Jay
    ACM TRANSACTIONS ON STORAGE, 2022, 18 (04)
  • [19] Learning to Rank Complex Biomedical Hypotheses for Accelerating Scientific Discovery
    Ding, Juncheng
    Dahal, Shailesh
    Adhikari, Bijaya
    Jha, Kishlay
    2024 IEEE 12TH INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS, ICHI 2024, 2024, : 285 - 293
  • [20] Accelerating Data-Driven Discovery With Scientific Asset Management
    Schuler, Robert E.
    Kesselman, Carl
    Czajkowski, Karl
    PROCEEDINGS OF THE 2016 IEEE 12TH INTERNATIONAL CONFERENCE ON E-SCIENCE (E-SCIENCE), 2016, : 31 - 40