| 1 | Generative artificial intelligence and its applications in materials science:Current situation and future perspectives显示文摘Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development. | Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi | 2023 | Journal of Materiomics2023，9，4： | 1 |
| 2 | Prediction Model for Non-topological Event Propagation in Social Networks显示文摘The spread of events happens all the time in social networks. The prediction of event propagation has received extensive attention in data mining community. In prior studies, topologies in social networks are usually exploited to predict the scope of event propagation. User’s action logs can be obtained in reality, but it is difficult to get topologies in social networks. In this paper, NTGP, a prediction model for non-topological event propagation, is proposed. Firstly a time decay sampling method was used to extract the walk paths from user’s action log, and then deep learning method was applied to learn the sampling paths and predict the future propagation range of the target event. Extensive experiments demonstrate effectiveness of NT-GP. | Zitu Liu Rui Wang Yong Liu | 2019 | 国际计算机前沿大会会议论文集2019，，1： | 0 |
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