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 material design.
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
GT4SD is an open-source library that uses generative models to accelerate scientific discovery in material design.
- Year
- 2022
- Venue
- arXiv 2022
- Authors
- 24
- Hosting
- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2207.03928v4ARXIV-DEFAULT
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Abstract
Authors
24Vijil ChenthamarakshanInkit PadhiPayel DasJannis BornOliver SchilterMatteo ManicaJoris CadowDimitrios ChristofidellisAshish DaveDean ClarkeYves Gaetan Nana TeukamGiorgio GiannoneSamuel C. HoffmanMatthew BuchanTimothy DonovanHsiang Han HsuFederico ZipoliAkihiro KishimotoLisa HamadaKarl WehdenLauren McHughAlexy KhrabrovSeiji TakedaJohn R. Smith