Nicholas Wagner

850 total citations
9 papers, 659 citations indexed

About

Nicholas Wagner is a scholar working on Materials Chemistry, Catalysis and Electrical and Electronic Engineering. According to data from OpenAlex, Nicholas Wagner has authored 9 papers receiving a total of 659 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Materials Chemistry, 2 papers in Catalysis and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Nicholas Wagner's work include Machine Learning in Materials Science (4 papers), Electronic and Structural Properties of Oxides (3 papers) and X-ray Diffraction in Crystallography (2 papers). Nicholas Wagner is often cited by papers focused on Machine Learning in Materials Science (4 papers), Electronic and Structural Properties of Oxides (3 papers) and X-ray Diffraction in Crystallography (2 papers). Nicholas Wagner collaborates with scholars based in United States, China and Germany. Nicholas Wagner's co-authors include James M. Rondinelli, Christian Morsczeck, Michael Giersig, Miguel A. Correa‐Duarte, José Rojas-Chapana, Michael Thie, Candace K. Chan, Ran Zhao, Danilo Puggioni and Qun Wei and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nano Letters and Chemistry of Materials.

In The Last Decade

Nicholas Wagner

8 papers receiving 648 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nicholas Wagner United States	8	359	267	146	82	67	9	659
Kangming Li China	15	309 0.9×	110 0.4×	195 1.3×	49 0.6×	42 0.6×	46	654
Aravind Rammohan United States	17	276 0.8×	402 1.5×	88 0.6×	151 1.8×	83 1.2×	23	1.1k
Teng Long China	13	314 0.9×	305 1.1×	243 1.7×	30 0.4×	119 1.8×	37	800
Zhenlin Zhang China	14	285 0.8×	235 0.9×	111 0.8×	52 0.6×	36 0.5×	41	652
Gustavo F. Trindade United Kingdom	19	244 0.7×	282 1.1×	207 1.4×	34 0.4×	35 0.5×	62	900
Yao Gong China	12	191 0.5×	220 0.8×	254 1.7×	14 0.2×	68 1.0×	22	559
Peiyi Li China	15	167 0.5×	173 0.6×	200 1.4×	36 0.4×	59 0.9×	60	658
Xiaojie Yang China	16	247 0.7×	416 1.6×	235 1.6×	61 0.7×	68 1.0×	50	854
Shashishekar P. Adiga India	22	584 1.6×	335 1.3×	574 3.9×	53 0.6×	67 1.0×	48	1.4k
Albert Lin United States	19	546 1.5×	342 1.3×	619 4.2×	26 0.3×	21 0.3×	32	1.0k

Countries citing papers authored by Nicholas Wagner

Since Specialization

Citations

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

Fields of papers citing papers by Nicholas Wagner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicholas Wagner

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

All Works

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9 of 9 papers shown

1.

Wagner, Nicholas, et al.. (2024). Lacuna Language Learning: Leveraging RNNs for Ranked Text Completion in Digitized Coptic Manuscripts. 61–70.

2.

Georgescu, Alexandru B., et al.. (2021). Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds. Chemistry of Materials. 33(14). 5591–5605. 34 indexed citations

3.

Yang, Lusann, Joel A. Haber, Zan Armstrong, et al.. (2021). Discovery of complex oxides via automated experiments and data science. Proceedings of the National Academy of Sciences. 118(37). 29 indexed citations

4.

Wagner, Nicholas, Ram Seshadri, & James M. Rondinelli. (2019). Property control from polyhedral connectivity in ABO3 oxides. Physical review. B.. 100(6). 13 indexed citations

5.

Wagner, Nicholas, Danilo Puggioni, & James M. Rondinelli. (2018). Learning from Correlations Based on Local Structure: Rare-Earth Nickelates Revisited. Journal of Chemical Information and Modeling. 58(12). 2491–2501. 15 indexed citations

6.

Wagner, Nicholas & James M. Rondinelli. (2016). Theory-Guided Machine Learning in Materials Science. Frontiers in Materials. 3. 142 indexed citations

7.

Li, Ying, Nicholas Wagner, Stephen K. Davidowski, et al.. (2015). Type I Clathrates as Novel Silicon Anodes: An Electrochemical and Structural Investigation. Advanced Science. 2(6). 1500057–1500057. 29 indexed citations

8.

Wagner, Nicholas, et al.. (2013). Electrochemical Cycling of Sodium‐Filled Silicon Clathrate. ChemElectroChem. 1(2). 347–353. 28 indexed citations

9.

Correa‐Duarte, Miguel A., Nicholas Wagner, José Rojas-Chapana, et al.. (2004). Fabrication and Biocompatibility of Carbon Nanotube-Based 3D Networks as Scaffolds for Cell Seeding and Growth. Nano Letters. 4(11). 2233–2236. 369 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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