Dane Morgan

34.2k total citations · 8 hit papers
409 papers, 21.7k citations indexed

About

Dane Morgan is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Dane Morgan has authored 409 papers receiving a total of 21.7k indexed citations (citations by other indexed papers that have themselves been cited), including 280 papers in Materials Chemistry, 120 papers in Electrical and Electronic Engineering and 77 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Dane Morgan's work include Electronic and Structural Properties of Oxides (64 papers), Nuclear Materials and Properties (60 papers) and Machine Learning in Materials Science (58 papers). Dane Morgan is often cited by papers focused on Electronic and Structural Properties of Oxides (64 papers), Nuclear Materials and Properties (60 papers) and Machine Learning in Materials Science (58 papers). Dane Morgan collaborates with scholars based in United States, United Kingdom and China. Dane Morgan's co-authors include Gerbrand Ceder, Yang Shao‐Horn, Anton Van der Ven, Yueh‐Lin Lee, Ryan Jacobs, Chris G. Van de Walle, A. F. Kohan, G. Ceder, Chris A. Marianetti and Izabela Szlufarska and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.

In The Last Decade

Dane Morgan

391 papers receiving 21.2k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

First-principles study of native point defects in ZnO

2000 1.5k citations Dane Morgan et al. profile →
Instability of Pt∕C Electrocatalysts in Proton Exchange Membrane Fuel Cells

2005 1.3k citations Yang Shao‐Horn, Dane Morgan et al. profile →
AFLOW: An automatic framework for high-throughput materials discovery

2012 1.1k citations Dane Morgan et al. Computational Materials Science profile →
Li Conductivity in Li[sub x]MPO[sub 4] (M = Mn, Fe, Co, Ni) Olivine Materials

2004 997 citations Dane Morgan et al. profile →
First-principles prediction of redox potentials in transition-metal compounds withLDA+U

2004 924 citations Dane Morgan et al. profile →
Instability of Supported Platinum Nanoparticles in Low-Temperature Fuel Cells

2007 800 citations Yang Shao‐Horn, Dane Morgan et al. profile →
Prediction of solid oxide fuel cell cathode activity with first-principles descriptors

2011 547 citations Yueh‐Lin Lee, Yang Shao‐Horn et al. profile →
Extracting accurate materials data from research papers with conversational language models and prompt engineering

2024 156 citations Maciej P. Polak, Dane Morgan Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dane Morgan United States	69	12.7k	10.1k	4.5k	3.7k	2.7k	409	21.7k
Nigel D. Browning United States	85	14.3k 1.1×	9.3k 0.9×	4.4k 1.0×	4.3k 1.2×	2.3k 0.8×	569	25.0k
Geoffroy Hautier United States	58	20.4k 1.6×	14.2k 1.4×	3.2k 0.7×	4.4k 1.2×	2.9k 1.1×	189	30.2k
Peng Gao China	83	12.8k 1.0×	14.1k 1.4×	4.0k 0.9×	5.4k 1.5×	1.7k 0.6×	583	24.9k
Jie Xiong China	79	9.5k 0.8×	13.0k 1.3×	5.8k 1.3×	3.2k 0.9×	903 0.3×	344	20.6k
Michael J. Aziz United States	70	9.1k 0.7×	13.4k 1.3×	5.4k 1.2×	2.6k 0.7×	2.8k 1.0×	362	22.3k
Shyue Ping Ong United States	81	22.7k 1.8×	20.0k 2.0×	2.8k 0.6×	3.6k 1.0×	3.9k 1.4×	194	37.2k
Naoya Shibata Japan	71	13.6k 1.1×	6.9k 0.7×	9.7k 2.2×	2.3k 0.6×	1.6k 0.6×	439	20.4k
William D. Richards United States	26	11.8k 0.9×	10.4k 1.0×	1.5k 0.3×	1.8k 0.5×	1.7k 0.6×	56	19.0k
Jeffrey W. Elam United States	87	16.4k 1.3×	15.1k 1.5×	4.3k 1.0×	3.3k 0.9×	1.7k 0.6×	407	26.4k

Countries citing papers authored by Dane Morgan

Since Specialization

Citations

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

Fields of papers citing papers by Dane Morgan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dane Morgan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shen, Chen, et al.. (2025). SuperSalt: equivariant neural network force fields for multicomponent molten salts system. Nature Communications. 16(1). 7280–7280.

Matanović, Ivana, et al.. (2024). Density functional theory calculations of the electronic structure and dielectric properties of metal oxide systems Al2O3, MgO, Cu2O, TiO2, WO3. Journal of Electron Spectroscopy and Related Phenomena. 278. 147512–147512. 1 indexed citations

Voyles, Paul M., et al.. (2024). Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization. Journal of Materiomics. 11(4). 100964–100964. 2 indexed citations

Ward, Logan, Steven R. Wangen, Marcus Schwarting, et al.. (2024). Foundry-ML - Software and Services to Simplify Accessto Machine Learning Datasets in Materials Science. The Journal of Open Source Software. 9(93). 5467–5467. 3 indexed citations

Sheikh, Md Sariful, Ryan Jacobs, Dane Morgan, & John H. Booske. (2024). Time dependence of SrVO3 thermionic electron emission properties. Journal of Applied Physics. 135(5). 1 indexed citations

Yu, Zheng, et al.. (2024). How close are the classical two-body potentials to ab initio calculations? Insights from linear machine learning based force matching. The Journal of Chemical Physics. 160(5). 2 indexed citations

Lian, Jason, et al.. (2024). Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion. Materials. 17(2). 510–510. 8 indexed citations

Lv, Xiu‐Liang, Patrick Sullivan, Wenjie Li, et al.. (2023). Modular dimerization of organic radicals for stable and dense flow battery catholyte. Nature Energy. 8(10). 1109–1118. 62 indexed citations

Ward, Logan, John H. Perepezko, Dan J. Thoma, et al.. (2022). Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features. Chemistry of Materials. 34(7). 2945–2954. 18 indexed citations

10.

Jacobs, Ryan, et al.. (2022). Demonstration of Low Work Function Perovskite SrVO₃ Using Thermionic Electron Emission. Advanced Functional Materials. 32(41). 15 indexed citations

11.

Szlufarska, Izabela, et al.. (2021). Molecular dynamic characteristic temperatures for predicting metallic glass forming ability. Computational Materials Science. 201. 110877–110877. 5 indexed citations

12.

Li, Xiangguo, Ben Blaiszik, Marcus Schwarting, et al.. (2021). Graph network based deep learning of bandgaps. The Journal of Chemical Physics. 155(15). 154702–154702. 16 indexed citations

13.

Wu, Dongxia, et al.. (2021). Multi defect detection and analysis of electron microscopy images with deep learning. Computational Materials Science. 199. 110576–110576. 38 indexed citations

14.

Jacobs, Ryan, Jonathan Hwang, Yang Shao‐Horn, & Dane Morgan. (2019). Assessing Correlations of Perovskite Catalytic Performance with Electronic Structure Descriptors. Chemistry of Materials. 31(3). 785–797. 149 indexed citations

15.

Almirall, Nathan, Peter Wells, Huibin Ke, et al.. (2019). On the Elevated Temperature Thermal Stability of Nanoscale Mn-Ni-Si Precipitates Formed at Lower Temperature in Highly Irradiated Reactor Pressure Vessel Steels. Scientific Reports. 9(1). 9587–9587. 40 indexed citations

16.

Xu, Shenzhen, Guangfu Luo, Ryan Jacobs, et al.. (2017). Ab Initio Modeling of Electrolyte Molecule Ethylene Carbonate Decomposition Reaction on Li(Ni,Mn,Co)O₂ Cathode Surface. ACS Applied Materials & Interfaces. 9(24). 20545–20553. 85 indexed citations

17.

Lee, Yueh‐Lin & Dane Morgan. (2015). Ab initio defect energetics of perovskite (001) surfaces for solid oxide fuel cells: A comparative study of LaMnO[subscript 3] versus SrTiO[subscript 3] and LaAlO[subscript 3]. Physical Review Letters. 1 indexed citations

18.

Mayeshiba, Tam & Dane Morgan. (2014). Strain effects on oxygen migration in perovskites. Physical Chemistry Chemical Physics. 17(4). 2715–2721. 72 indexed citations

19.

Morgan, Dane, et al.. (2008). Surface and Bulk Characteristics of Cesium Iodide (CsI) coated Carbon (C) Fibers for High Power Microwave (HPM) Field Emission Cathodes. Bulletin of the American Physical Society. 50. 1 indexed citations

20.

Morgan, Dane, Darren G. Chertkoff, Dougal A. Jerram, J. P. Davidson, & L. Francalanci. (2005). What's in a Whole Rock Analysis? Integrating Crystal Size Distributions and Micro-Scale Isotopic Variations at Stromboli Volcano. AGUFM. 2005. 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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