Accelerating material design with the generative toolkit for scientific discovery
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Published:2023-05-01
Issue:1
Volume:9
Page:
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ISSN:2057-3960
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Container-title:npj Computational Materials
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language:en
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Short-container-title:npj Comput Mater
Author:
Manica MatteoORCID, Born JannisORCID, Cadow JorisORCID, Christofidellis DimitriosORCID, Dave Ashish, Clarke Dean, Teukam Yves Gaetan Nana, Giannone Giorgio, Hoffman Samuel C., Buchan Matthew, Chenthamarakshan VijilORCID, Donovan Timothy, Hsu Hsiang Han, Zipoli Federico, Schilter Oliver, Kishimoto Akihiro, Hamada Lisa, Padhi Inkit, Wehden Karl, McHugh Lauren, Khrabrov Alexy, Das PayelORCID, Takeda Seiji, Smith John R.
Abstract
AbstractWith 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.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
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