Experimental constraints on mantle sulfide melting up to 8 GPa Z Zhang, MM Hirschmann American Mineralogist 101 (1), 181-192, 2016 | 81 | 2016 |
Mantle upwelling during Permian to Triassic in the northern margin of the North China Craton: Constraints from southern Inner Mongolia Z Zhang, H Zhang, J Shao, J Ying, Y Yang, M Santosh Journal of Asian Earth Sciences 79, 112-129, 2014 | 43 | 2014 |
Primordial metallic melt in the deep mantle Z Zhang, SM Dorfman, J Labidi, S Zhang, M Li, M Manga, L Stixrude, ... Geophysical Research Letters 43 (8), 3693-3699, 2016 | 42 | 2016 |
Carbon-saturated monosulfide melting in the shallow mantle: solubility and effect on solidus Z Zhang, N Lentsch, MM Hirschmann Contributions to Mineralogy and Petrology 170, 1-13, 2015 | 29 | 2015 |
Guangtoushan granites and their enclaves: Implications for Triassic mantle upwelling in the northern margin of the North China Craton Z Zhang, H Zhang, J Ying, Y Yang, M Santosh Lithos 149, 174-187, 2012 | 26 | 2012 |
Experimental determination of carbon solubility in Fe-Ni-S melts Z Zhang, P Hastings, A Von der Handt, MM Hirschmann Geochimica et Cosmochimica Acta 225, 66-79, 2018 | 24 | 2018 |
Electrical investigation of natural lawsonite and application to subduction contexts A Pommier, Q Williams, RL Evans, I Pal, Z Zhang Journal of Geophysical Research: Solid Earth 124 (2), 1430-1442, 2019 | 19 | 2019 |
An experimental study of Fe–Ni exchange between sulfide melt and olivine at upper mantle conditions: implications for mantle sulfide compositions and phase equilibria Z Zhang, A von der Handt, MM Hirschmann Contributions to Mineralogy and Petrology 173, 1-18, 2018 | 17 | 2018 |
Estimating ferric iron content in clinopyroxene using machine learning models W Huang, Y Lyu, M Du, C He, S Gao, R Xu, Q Xia, J ZhangZhou American Mineralogist 107 (10), 1886-1900, 2022 | 13 | 2022 |
Electrical investigation of metal‐olivine systems and application to the deep interior of Mercury Z Zhang, A Pommier Journal of Geophysical Research: Planets 122 (12), 2702-2718, 2017 | 13 | 2017 |
Carbon storage in Fe-Ni-S liquids in the deep upper mantle and its relation to diamond and Fe-Ni alloy precipitation Z Zhang, T Qin, A Pommier, MM Hirschmann Earth and Planetary Science Letters 520, 164-174, 2019 | 12 | 2019 |
Machine learning investigation of clinopyroxene compositions to evaluate and predict mantle metasomatism worldwide B Qin, F Huang, S Huang, A Python, Y Chen, J ZhangZhou Journal of Geophysical Research: Solid Earth 127 (5), e2021JB023614, 2022 | 11 | 2022 |
Machine learning for identification of primary water concentrations in mantle pyroxene H Chen, C Su, YQ Tang, AZ Li, SS Wu, QK Xia, J ZhangZhou Geophysical Research Letters 48 (18), e2021GL095191, 2021 | 8 | 2021 |
金刚石与深部碳循环 张舟, 张宏福 地学前缘 18 (3), 268-283, 2011 | 8 | 2011 |
基性, 超基性岩: 二氧化碳地质封存的新途径 张舟, 张宏福 地球科学: 中国地质大学学报 37 (1), 156-162, 2012 | 6 | 2012 |
Carbon in the deep upper mantle and transition zone under reduced conditions: Insights from high-pressure experiments and machine learning models J Lei, S Sen, Y Li, J ZhangZhou Geochimica et Cosmochimica Acta 332, 88-102, 2022 | 5 | 2022 |
Refined estimation of Li in mica by a machine learning method L Wang, C Su, LQ Wang, J ZhangZhou, QK Xia, QY Wang American Mineralogist 107 (6), 1034-1044, 2022 | 4 | 2022 |
Identifying serpentine minerals by their chemical compositions with machine learning S Ji, F Huang, S Wang, P Gupta, W Seyfried Jr, H Zhang, X Chu, W Cao, ... American Mineralogist 109 (2), 315-324, 2024 | 1 | 2024 |
Predicting sulfide precipitation in magma oceans on Earth, Mars and the Moon using machine learning J ZhangZhou, Y Li, P Chowdhury, S Sen, U Ghosh, Z Xu, J Liu, Z Wang, ... Geochimica et Cosmochimica Acta 366, 237-249, 2024 | 1 | 2024 |
Geochemistry π: Automated machine learning Python framework for tabular data J ZhangZhou, C He, J Sun, J Zhao, Y Lyu, S Wang, W Zhao, A Li, X Ji, ... Geochemistry, Geophysics, Geosystems 25 (1), e2023GC011324, 2024 | 1 | 2024 |