学习迁移
变压器
计算机科学
赫克反应
人工智能
机器学习
化学
工程类
有机化学
电气工程
催化作用
电压
钯
作者
Ling Wang,Chengyun Zhang,Renren Bai,Jianjun Li,Hongliang Duan
摘要
A proof-of-concept methodology for addressing small amounts of chemical data using transfer learning is presented. We demonstrate this by applying transfer learning combined with the transformer model to small-dataset Heck reaction prediction. Introducing transfer learning significantly improved the accuracy of the transformer-transfer learning model (94.9%) over that of the transformer-baseline model (66.3%).
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