The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our a...The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.展开更多
Electronic structures in two kinds of boron structures are investigated by the first-principle density func- tional theory （DFT） calculations. One structure is from theoretical prediction, and the other is from expe...Electronic structures in two kinds of boron structures are investigated by the first-principle density func- tional theory （DFT） calculations. One structure is from theoretical prediction, and the other is from experimental in- vestigation. Binding energy calculations suggest that the boron structure designed from theory is more stable than that made by experiment. Elastic constants calculations show that both structures are mechanically stable. The electronic structure results show that the theoretical designed structure exhibits semi-metal behavior, while the other structure exhibits metMlic character. No magnetic phenomenal is discovered from them. All the calculations are carried out by the first principles calculation through the MatC1oud platform, which is developed by our research group.展开更多
基金supported by the Informatization Plan of the Chinese Academy of Sciences (Grant No. CASWX2023SF-0101)the Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-7025)+1 种基金the Youth Innovation Promotion Association CAS (Grant No. 2021167)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000)。
文摘The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.
基金Supported by National Natural Science Foundation of China under Grant No.11547177
文摘Electronic structures in two kinds of boron structures are investigated by the first-principle density func- tional theory （DFT） calculations. One structure is from theoretical prediction, and the other is from experimental in- vestigation. Binding energy calculations suggest that the boron structure designed from theory is more stable than that made by experiment. Elastic constants calculations show that both structures are mechanically stable. The electronic structure results show that the theoretical designed structure exhibits semi-metal behavior, while the other structure exhibits metMlic character. No magnetic phenomenal is discovered from them. All the calculations are carried out by the first principles calculation through the MatC1oud platform, which is developed by our research group.
