Danni Yang

1.7k total citations
55 papers, 1.3k citations indexed

About

Danni Yang is a scholar working on Mechanical Engineering, Materials Chemistry and Aerospace Engineering. According to data from OpenAlex, Danni Yang has authored 55 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Mechanical Engineering, 28 papers in Materials Chemistry and 16 papers in Aerospace Engineering. Recurrent topics in Danni Yang's work include High Entropy Alloys Studies (17 papers), High-Temperature Coating Behaviors (11 papers) and Advanced materials and composites (10 papers). Danni Yang is often cited by papers focused on High Entropy Alloys Studies (17 papers), High-Temperature Coating Behaviors (11 papers) and Advanced materials and composites (10 papers). Danni Yang collaborates with scholars based in China, United States and Australia. Danni Yang's co-authors include Tianyi Han, Mingqing Liao, Zhonghong Lai, Nan Qu, Jingchuan Zhu, Yong Liu, David K. Matlock, E. L. Brown, G. Krauß and Sheng Zhang and has published in prestigious journals such as Applied Physics Letters, Advanced Functional Materials and ACS Applied Materials & Interfaces.

In The Last Decade

Danni Yang

51 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Danni Yang China 21 806 674 325 237 172 55 1.3k
R. K. Mishra India 19 554 0.7× 395 0.6× 199 0.6× 231 1.0× 235 1.4× 76 1.1k
Milo V. Kral New Zealand 22 993 1.2× 664 1.0× 340 1.0× 204 0.9× 205 1.2× 64 1.3k
Benoît Panicaud France 17 584 0.7× 492 0.7× 344 1.1× 241 1.0× 40 0.2× 87 986
Yanzhou Ji United States 25 968 1.2× 1.3k 1.9× 536 1.6× 308 1.3× 167 1.0× 73 2.1k
Dongsheng Xu China 27 1.6k 1.9× 1.8k 2.6× 248 0.8× 538 2.3× 97 0.6× 95 2.3k
Susumu Onaka Japan 21 980 1.2× 981 1.5× 237 0.7× 639 2.7× 40 0.2× 133 1.5k
Khershed P. Cooper United States 15 715 0.9× 590 0.9× 281 0.9× 297 1.3× 42 0.2× 52 1.1k
F.D. Fischer Austria 20 829 1.0× 717 1.1× 212 0.7× 469 2.0× 46 0.3× 42 1.2k
Caizhi Zhou United States 23 1.1k 1.3× 983 1.5× 272 0.8× 528 2.2× 39 0.2× 66 1.6k
Yao Shen China 28 1.3k 1.6× 1.3k 1.9× 300 0.9× 656 2.8× 459 2.7× 112 2.3k

Countries citing papers authored by Danni Yang

Since Specialization
Citations

This map shows the geographic impact of Danni Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Danni Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danni Yang more than expected).

Fields of papers citing papers by Danni Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danni Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Danni Yang. The network helps show where Danni Yang may publish in the future.

Co-authorship network of co-authors of Danni Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Danni Yang. A scholar is included among the top collaborators of Danni Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Danni Yang. Danni Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Danni, et al.. (2025). Source Causal Connectivity Noninvasively Predicting Surgical Outcomes of Drug‐Refractory Epilepsy. CNS Neuroscience & Therapeutics. 31(1). e70196–e70196. 1 indexed citations
2.
Akhtar, Waseem, Nan Qu, Ishfaq Ahmed, et al.. (2025). Discovery and design of Equiatomic Heusler alloys magnetic properties using machine learning with DFT approach. Materials Today Communications. 46. 112571–112571. 1 indexed citations
3.
Akhtar, Waseem, et al.. (2025). Accelerating the design of TiZrNbTa-based refractory high-entropy alloys with enhanced strength and ductility using machine learning and DFT calculations. Materials Today Communications. 46. 112367–112367. 2 indexed citations
4.
Yang, Danni, Nan Qu, Mingqing Liao, et al.. (2025). Exceptional thermal stability and the strengthening mechanism of ultrafine-grained FexCrNiAl medium-entropy alloys synthesized via powder metallurgy. Journal of Alloys and Compounds. 1036. 182026–182026.
5.
Akhtar, Waseem, et al.. (2025). Prediction of low-modulus Ti-based refractory high-entropy alloys for orthopedic implants using machine learning and DFT calculations. Physica B Condensed Matter. 715. 417560–417560. 2 indexed citations
6.
Yang, Tianyu, Yifan Cai, Tianping Huang, et al.. (2024). A telomere-to-telomere gap-free reference genome assembly of avocado provides useful resources for identifying genes related to fatty acid biosynthesis and disease resistance. Horticulture Research. 11(7). uhae119–uhae119. 6 indexed citations
7.
Yang, Danni, Mingqing Liao, Jingtao Huang, et al.. (2024). Synthesis and Phase Evolution of a Nanocrystalline FexCrNiAl (x = 1.0, 0.5, 0.25) High-Entropy Alloys by Mechanical Alloying. Materials. 17(24). 6061–6061. 1 indexed citations
8.
9.
Han, Tianyi, Jiaying Chen, Nan Qu, et al.. (2023). Effect of cooling rate on microstructure and mechanical properties of AlCrFe2Ni2 medium entropy alloy fabricated by laser powder bed fusion. Journal of Materials Research and Technology. 25. 4063–4073. 24 indexed citations
10.
Qu, Nan, Yong Liu, Yan Zhang, et al.. (2022). Machine learning guided phase formation prediction of high entropy alloys. Materials Today Communications. 32. 104146–104146. 20 indexed citations
11.
Qu, Nan, Yan Zhang, Yong Liu, et al.. (2022). Accelerating phase prediction of refractory high entropy alloys via machine learning. Physica Scripta. 97(12). 125710–125710. 9 indexed citations
12.
Liao, Mingqing, Yong Liu, Fei Zhou, et al.. (2022). A high-efficient strain-stress method for calculating higher-order elastic constants from first-principles. Computer Physics Communications. 280. 108478–108478. 17 indexed citations
13.
Jin, Xuebo, Tingli Su, Danni Yang, et al.. (2021). Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy. Entropy. 23(2). 219–219. 36 indexed citations
14.
Shi, Tian, Xuewu Li, Hongxing Wang, et al.. (2020). One‐step preparation of the superhydrophobic Al alloy surface with enhanced corrosion and wear resistance. Materials and Corrosion. 72(5). 904–911. 9 indexed citations
15.
Qu, Nan, Yong Liu, Mingqing Liao, et al.. (2019). Ultra-high temperature ceramics melting temperature prediction via machine learning. Ceramics International. 45(15). 18551–18555. 61 indexed citations
16.
Shi, Tian, Xuewu Li, Hongxing Wang, et al.. (2018). One-step machining of hierarchical micro-nano structures on Al-Mg alloy as superamphiphobic substrate for amplified SERS trace detection. Materials Research Express. 6(4). 46510–46510. 1 indexed citations
17.
Ba, You, Yan Liu, Peisen Li, et al.. (2018). Spatially Resolved Electric‐Field Manipulation of Magnetism for CoFeB Mesoscopic Discs on Ferroelectrics. Advanced Functional Materials. 28(11). 41 indexed citations
18.
Wang, Jing, Houbing Huang, Qinghua Zhang, et al.. (2017). Nanoscale Bandgap Tuning across an Inhomogeneous Ferroelectric Interface. ACS Applied Materials & Interfaces. 9(29). 24704–24710. 16 indexed citations
19.
Wu, Ying, et al.. (2003). In situ tensile deformation and fracture behavior of Ti–24Al–14Nb–3V–0.5Mo alloy with various microstructures. Intermetallics. 12(1). 43–53. 14 indexed citations
20.
Mao, Jinhua, et al.. (2002). Microstructural characterization of NiCr1BSiC laser clad layer on titanium alloy substrate. Surface and Coatings Technology. 150(2-3). 199–204. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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