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| 1 | Highly green fluorescent Nb2C MXene quantum dots for Cu2+ion sensing and cell imaging显示文摘Niobium carbide MXene quantum dots(Nb2 C MQDs)derived from 2 D Nb2 CTx(MXene)are the rising-star material recently.Herein,a sulfur and nitrogen co-doped Nb2 C MQDs(S,N-MQDs)were synthesized through a hydrothermal method.The obtained Nb2 C MQDs have excellent green fluorescence with a quantum yield(QY)of 17.25%.In addition,they exhibited excitatio n-dependent photoluminescence,antiphotobleaching and dispersion stability.They emit light at 520 nm when excited at 390 nm.The Nb2 C MQDs could be successfully applied to copper ion detection with detection limit of 2μmol/L and Caco-2 cells imaging. | Xiang Yan Junfei Ma Kaixuan Yu Jiapeng Li Lei Yang Jjiaqi Liu Juncheng Wang Lulu Cai | 2020 | Chinese Chemical Letters2020，31，12： | 3 |
| 2 | Vision for energy material design:A roadmap for integrated data-driven modeling显示文摘The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed. | Zhilong Wang Yanqiang Han Junfei Cai An Chen Jinjin Li | 2022 | Journal of Energy Chemistry2022，31，8： | 2 |
| 3 | Deep Learning Accelerates the Discovery of Two- Dimensional Catalysts for Hydrogen Evolution Reaction显示文摘Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space. | Sicheng Wu Zhilong Wang Haikuo Zhang Junfei Cai Jinjin Li | 2023 | Energy & Environmental Materials2023，6，1： | 1 |
| 4 | Potentials of crop residues for commercial energy production in China: A geographic and economic analysis显示文摘 | Huanguang Qiu Laixiang Sun Xinliang Xu Yaqing Cai Junfei Bai | 2014 | Biomass and Bioenergy2014，，： | 1 |
| 5 | Unsupervised discovery of thin-film photovoltaic materials from unlabeled data显示文摘Quaternary chalcogenide semiconductors(I_(2)-II-IV-X_(4))are key materials for thin-film photovoltaics(PVs)to alleviate the energy crisis.Scaling up of PVs requires the discovery of I_(2)-II-IV-X_(4) with good photoelectric properties;however,the structure search space is significantly large to explore exhaustively.The scarcity of available data impedes even many machine learning(ML)methods.Here,we employ the unsupervised learning(UL)method to discover I2-II-IV-X4 that alleviates the challenge of data scarcity.We screen all the I_(2)-II-IV-X_(4) from the periodic table as the initial data and finally select eight candidates through UL.As predicted by ab initio calculations,they exhibit good optical conversion efficiency,strong optical responses,and good thermal stabilities at room temperatures.This typical case demonstrates the potential of UL in material discovery,which overcomes the limitation of data scarcity,and shortens the computational screening cycle of I_(2)-II-IV-X_(4) by~12.1 years,providing a research avenue for rapid material discovery. | Zhilong Wang Junfei Cai Qingxun Wang SiCheng Wu Jinjin Li | 2021 | npj Computational Materials2021，，1： | 1 |
| 6 | An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction显示文摘Accurate regulation of two-dimensional materials has become an effective strategy to develop a wide range of catalytic applications.The introduction of heterogeneous components has a significant impact on the performance of materials,which makes it difficult to discover and understand the structure-property relationships at the atomic level.Here,we developed a novel and efficient ensemble learning classifier with synthetic minority oversampling technique(SMOTE) to discover all possible arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction(HER).A total of 850 doped arsenenes were collected as a database and 140 modified arsenene materials with different doping atoms and doping sites were identified as promising candidate catalysts for HER,with a machine learning prediction accuracy of 81%.Based on the results of machine learning,we proposed 13 low-cost and easily synthesized two-dimensional Fe-doped arsenene catalytic materials that are expected to contribute to high-efficient HER.The proposed ensemble method achieved high prediction accuracy,but millions of times faster to predict Gibbs free energies and only required a small amount of data.This study indicates that the presented ensemble learning classifier is capable of screening high-efficient catalysts,and can be further extended to predict other two-dimensional catalysts with delicate regulation. | An Chen Junfei Cai Zhilong Wang Yanqiang Han Simin Ye Jinjin Li | 2023 | Journal of Energy Chemistry2023，，3： | 0 |
| 7 | An end-to-end artificial intelligence platform enables real-time assessment of superionic conductors显示文摘Superionic conductors(SCs)exhibiting low ion migration activation energy(Ea)are critical to the performance of electrochemical energy storage devices such as solid-state batteries and fuel cells.However,it is challenging to obtain Ea experimentally and theoretically,and the artificial intelligence(AI)method is expected to bring a breakthrough in predicting Ea.Here,we proposed an AI platform(named AI-IMAE)to predict the Ea of cation and anion conductors,including Li^(+),Na^(+),Ag^(+),Al^(3+),Mg^(2+),Zn^(2+),Cu^((2)+),F^(−),and O^(2−),which is~105 times faster than traditional methods.The proposed AI-IMAE is based on crystal graph neural network models and achieves a holistic average absolute error of 0.19 eV,a median absolute error of 0.09 eV,and a Pearson coefficient of 0.92.Using AI-IMAE,we rapidly discovered 316 promising SCs as solid-state electrolytes and 129 SCs as cathode materials from 144,595 inorganic compounds.AI-IMAE is expected to completely solve the challenge of time-consuming Ea prediction and blaze a new trail for large-scale studies of SCs with excellent performance.As more experimental and high-precision theoretical data become available,AI-IMAE can train custom models and transfer the existing models to new models through transfer learning to constantly meet more demands. | Zhilong Wang Yanqiang Han Junfei Cai An Chen Jinjin Li | 2023 | SmartMat2023，4，6： | 0 |
| 8 | AlphaMat: a material informatics hub connecting data, features, models and applications显示文摘The development of modern civil industry,energy and information technology is inseparable from the rapid explorations of new materials.However,only a small fraction of materials being experimentally/computationally studied in a vast chemical space.Artificial intelligence(AI)is promising to address this gap,but faces many challenges,such as data scarcity and inaccurate material descriptors.Here,we develop an AI platform,AlphaMat,that can complete data preprocessing and downstream AI models.With high efficiency and accuracy,AlphaMat exhibits strong powers to model typical 12 material attributes(formation energy,band gap,ionic conductivity,magnetism,bulk modulus,etc.).AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains.AlphaMat does not require users to have strong programming experience,and its effective use will facilitate the development of materials informatics,which is of great significance for the implementation of AI for Science(AI4S). | Zhilong Wang An Chen Kehao Tao Junfei Cai Yanqiang Han Jing Gao Simin Ye Shiwei Wang Imran Ali Jinjin Li | 2023 | npj Computational Materials2023，，1： | 0 |
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