Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization

Generalizing real-world data has been one of the most difficult challenges for application of machine learning (ML) in practice. Most ML works focused on improvements in algorithms and feature representations. However, the data quality, as the foundation of ML, has been largely overlooked, also lead...

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Bibliographic Details
Main Author:	Songran Yang (6332597) (author)
Other Authors:	Ming Sun (30856) (author), Chaojie Shi (11472639) (author), Yiran Liu (5433908) (author), Yanzhi Guo (347654) (author), Yijing Liu (1847053) (author), Zhiyun Lu (1578337) (author), Yan Huang (46805) (author), Xuemei Pu (504405) (author)
Published:	2024
Subjects:	Biochemistry Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified stratified sampling based four molecular descriptor ensemble framework consisting >- fold division screen efficient oscs difficult reorganization energy ml works focused data split technique real osc molecules osc molecules oscs ), difficult challenges different osc ocs molecules like molecules 989 molecules world data wide range test platform structure characteristics strongly validated reliable tool processing methods prediction task prediction accuracy organic semiconductors navigated strategy ml fields machine learning local optimization largely overlooked k </ high reliability feature representations feature representation diverse structures data set data evaluation complementary strategy chemical nature chemical intuition charge mobility also leading
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Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization

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