Weiyi Xia

701 total citations
36 papers, 494 citations indexed

About

Weiyi Xia is a scholar working on Materials Chemistry, Electronic, Optical and Magnetic Materials and Condensed Matter Physics. According to data from OpenAlex, Weiyi Xia has authored 36 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Materials Chemistry, 13 papers in Electronic, Optical and Magnetic Materials and 9 papers in Condensed Matter Physics. Recurrent topics in Weiyi Xia's work include Machine Learning in Materials Science (12 papers), 2D Materials and Applications (7 papers) and Rare-earth and actinide compounds (5 papers). Weiyi Xia is often cited by papers focused on Machine Learning in Materials Science (12 papers), 2D Materials and Applications (7 papers) and Rare-earth and actinide compounds (5 papers). Weiyi Xia collaborates with scholars based in United States, China and Japan. Weiyi Xia's co-authors include Peihong Zhang, Yabei Wu, Weiwei Gao, Yujuan Zhang, Xiang Gao, Wei Ren, Fanhao Jia, Cai‐Zhuang Wang, Renhai Wang and Chao Zhang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Weiyi Xia

33 papers receiving 482 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Weiyi Xia United States	13	397	163	85	77	60	36	494
Mei Zhou China	11	559 1.4×	281 1.7×	110 1.3×	84 1.1×	36 0.6×	34	619
Federico Bianchini Norway	11	323 0.8×	234 1.4×	66 0.8×	112 1.5×	29 0.5×	21	479
Rafikul Ali Saha Belgium	9	246 0.6×	216 1.3×	66 0.8×	35 0.5×	36 0.6×	32	423
Liyuan Dong China	13	469 1.2×	357 2.2×	123 1.4×	63 0.8×	39 0.7×	27	583
Jaafar Jalilian Iran	19	690 1.7×	287 1.8×	148 1.7×	72 0.9×	48 0.8×	53	836
Genliang Han China	15	214 0.5×	196 1.2×	154 1.8×	153 2.0×	53 0.9×	32	451
Ariana Ray United States	5	624 1.6×	203 1.2×	104 1.2×	145 1.9×	64 1.1×	12	761
Fernando Salazar Mexico	14	582 1.5×	321 2.0×	64 0.8×	103 1.3×	26 0.4×	49	697
Dmitry V. Rybkovskiy Russia	14	573 1.4×	307 1.9×	96 1.1×	104 1.4×	18 0.3×	33	669

Countries citing papers authored by Weiyi Xia

Since Specialization

Citations

This map shows the geographic impact of Weiyi Xia'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 Weiyi Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weiyi Xia more than expected).

Fields of papers citing papers by Weiyi Xia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Weiyi Xia. 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 Weiyi Xia. The network helps show where Weiyi Xia may publish in the future.

Co-authorship network of co-authors of Weiyi Xia

This figure shows the co-authorship network connecting the top 25 collaborators of Weiyi Xia. A scholar is included among the top collaborators of Weiyi Xia 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 Weiyi Xia. Weiyi Xia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Xia, Weiyi, P. C. Canfield, R. A. Ribeiro, et al.. (2025). Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs. Physical Review Materials. 9(5). 1 indexed citations

Tang, Ling, et al.. (2025). Synthesis challenges, thermodynamic stability, and growth kinetics of La–Si–P ternary compounds. Journal of Materials Chemistry A. 13(46). 39982–39991.

Sun, Huaijun, Chao Zhang, Weiyi Xia, et al.. (2025). Prediction of lanthanide-containing ternary compounds. Computational Materials Science. 251. 113717–113717.

Xia, Weiyi, Zhuo Ye, Feng Zhang, et al.. (2025). exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design. The Journal of Open Source Software. 10(115). 8879–8879.

Xia, Weiyi, et al.. (2025). Search for Stable and Low-Energy Ce–Co–Cu Ternary Compounds Using Machine Learning. Inorganic Chemistry. 64(20). 10161–10169. 1 indexed citations

Xia, Weiyi, Masahiro Sakurai, Renhai Wang, et al.. (2024). Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy. SHILAP Revista de lepidopterología. 2(4). 2 indexed citations

Xia, Weiyi, Cai‐Zhuang Wang, & Vladimir Antropov. (2024). Giant magnetic anisotropy of Pb atoms in 3d-based magnets. Physical review. B.. 110(18). 1 indexed citations

Tang, Zhao, et al.. (2023). Stacking up Electron-Rich and Electron-Deficient Monolayers to Achieve Extraordinary Mid- to Far-Infrared Excitonic Absorption: Interlayer Excitons in the C3B/C3N Bilayer. Physical Review Applied. 19(4). 2 indexed citations

Wang, Renhai, Weiyi Xia, Tyler J. Slade, et al.. (2022). Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements. npj Computational Materials. 8(1). 13 indexed citations

10.

Wu, Yabei, Zhao Tang, Weiyi Xia, et al.. (2022). Prediction of protected band edge states and dielectric tunable quasiparticle and excitonic properties of monolayer MoSi2N4. npj Computational Materials. 8(1). 39 indexed citations

11.

Tang, Zhao, Yabei Wu, Weiyi Xia, et al.. (2022). Giant Narrow-Band Optical Absorption and Distinctive Excitonic Structures of Monolayer C3N and C3B. Physical Review Applied. 17(3). 15 indexed citations

12.

Sun, Huaijun, Chao Zhang, Weiyi Xia, et al.. (2022). Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements. Inorganic Chemistry. 61(42). 16699–16706. 10 indexed citations

13.

Tang, Zhao, et al.. (2021). Quasiparticle band structures of the 4d perovskite oxides SrZrO3 and BaZrO3. Physical review. B.. 104(19). 5 indexed citations

14.

Tang, Zhao, et al.. (2021). Quasiparticle band structure of SrTiO3 and BaTiO3: A combined LDA+U and G0W0 approach. Physical review. B.. 103(3). 7 indexed citations

15.

Xia, Weiyi, Weiwei Gao, Yabei Wu, et al.. (2020). Combined subsampling and analytical integration for efficient large-scale GW calculations for 2D systems. npj Computational Materials. 6(1). 14 indexed citations

16.

Zhang, Yujuan, Weiyi Xia, Yabei Wu, & Peihong Zhang. (2019). Prediction of MXene based 2D tunable band gap semiconductors: GW quasiparticle calculations. Nanoscale. 11(9). 3993–4000. 83 indexed citations

17.

Wu, Yabei, Weiyi Xia, Weiwei Gao, et al.. (2018). Quasiparticle electronic structure of honeycomb C ₃ N: from monolayer to bulk. 2D Materials. 6(1). 15018–15018. 21 indexed citations

18.

Gao, Weiwei, Weiyi Xia, Xiang Gao, & Peihong Zhang. (2017). Speeding up GW Calculations for Large Scale Quasiparticle Predictions. Bulletin of the American Physical Society. 2017. 2 indexed citations

19.

Gao, Weiwei, Weiyi Xia, Xiang Gao, & Peihong Zhang. (2016). Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions. Scientific Reports. 6(1). 36849–36849. 61 indexed citations

20.

Xia, Weiyi, et al.. (2007). Biopolymer composites reinforced with fractioned wheat straw. UCL Discovery (University College London). 1 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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