首页|Knowledge-reused transfer learning for molecular and materials science
Knowledge-reused transfer learning for molecular and materials science
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Leveraging big data analytics and advanced algorithms to accelerate and optimize the process of molec-ular and materials design,synthesis,and application has revolutionized the field of molecular and mate-rials science,allowing researchers to gain a deeper understanding of material properties and behaviors,leading to the development of new materials that are more efficient and reliable.However,the difficulty in constructing large-scale datasets of new molecules/materials due to the high cost of data acquisition and annotation limits the development of conventional machine learning(ML)approaches.Knowledge-reused transfer learning(TL)methods are expected to break this dilemma.The application of TL lowers the data requirements for model training,which makes TL stand out in researches addressing data quality issues.In this review,we summarize recent progress in TL related to molecular and materials.We focus on the application of TL methods for the discovery of advanced molecules/materials,particularly,the con-struction of TL frameworks for different systems,and how TL can enhance the performance of models.In addition,the challenges of TL are also discussed.
NETL
NSTL
万方数据
