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...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.展开更多
基金The authors acknowledge Helena Montenegro,Yoel Shoshan,Nicolai Ree,Miruna Cretu and Helder Lopes for their open-source contributions to the GT4SD.
文摘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.
