Advances in machine learning have transformed materials discovery,yet challenges remain due to the lack of informatics-ready data and the complexity of numerical descriptors.Scientific knowledge is scattered across pu...Advances in machine learning have transformed materials discovery,yet challenges remain due to the lack of informatics-ready data and the complexity of numerical descriptors.Scientific knowledge is scattered across publications,making comprehensive data extraction difficult.This study presents a large language model(LLM)-driven framework to accelerate organic solar cell(OSC)materials discovery by extracting structured data from literature and predicting device performance using natural language embeddings.Trained on a curated dataset of 422 OSC devices,the fine-tuned LLM demonstrated strong predictive accuracy across key performance metrics:power conversion efficiency(PCE,R^(2):0.87),short-circuit current(JSC,R^(2):0.82),open-circuit voltage(VOC,R^(2):0.89),and fill factor(FF,R^(2):0.59).The models are then used to explore the space of 1.4 million combinations of materials,experimental variables and device architectures.The analysis provides data-driven design guidelines,identifying optimal donor-acceptor combinations and processing conditions that consistently yield higher device performance.展开更多
The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In ...The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.展开更多
基金supported by the Office of Naval Research through grants N00014-19-1-2103 and N00014-20-1-2175.
文摘Advances in machine learning have transformed materials discovery,yet challenges remain due to the lack of informatics-ready data and the complexity of numerical descriptors.Scientific knowledge is scattered across publications,making comprehensive data extraction difficult.This study presents a large language model(LLM)-driven framework to accelerate organic solar cell(OSC)materials discovery by extracting structured data from literature and predicting device performance using natural language embeddings.Trained on a curated dataset of 422 OSC devices,the fine-tuned LLM demonstrated strong predictive accuracy across key performance metrics:power conversion efficiency(PCE,R^(2):0.87),short-circuit current(JSC,R^(2):0.82),open-circuit voltage(VOC,R^(2):0.89),and fill factor(FF,R^(2):0.59).The models are then used to explore the space of 1.4 million combinations of materials,experimental variables and device architectures.The analysis provides data-driven design guidelines,identifying optimal donor-acceptor combinations and processing conditions that consistently yield higher device performance.
基金supported by the National Natural Science Foundation of China(No.62302213)Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education,China.
文摘The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.
