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

来源：

NPJ COMPUTATIONAL MATERIALS | 2023年 / 9卷 / 01期

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

页数：6

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