James E. Saal

9.4k total citations · 3 hit papers
76 papers, 7.4k citations indexed

About

James E. Saal is a scholar working on Materials Chemistry, Mechanical Engineering and Aerospace Engineering. According to data from OpenAlex, James E. Saal has authored 76 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Materials Chemistry, 31 papers in Mechanical Engineering and 14 papers in Aerospace Engineering. Recurrent topics in James E. Saal's work include Machine Learning in Materials Science (21 papers), High Entropy Alloys Studies (14 papers) and High-Temperature Coating Behaviors (12 papers). James E. Saal is often cited by papers focused on Machine Learning in Materials Science (21 papers), High Entropy Alloys Studies (14 papers) and High-Temperature Coating Behaviors (12 papers). James E. Saal collaborates with scholars based in United States, France and China. James E. Saal's co-authors include Chris Wolverton, Bryce Meredig, Scott Kirklin, Muratahan Aykol, Jeff W. Doak, Alexander Thompson, Stefan Rühl, Zi‐Kui Liu, John R. Scully and Pin Lu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Journal of Applied Physics.

In The Last Decade

James E. Saal

74 papers receiving 7.2k citations

Hit 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.

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

2013 1.8k citations James E. Saal, Muratahan Aykol et al. JOM profile →
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

2015 1.7k citations James E. Saal, Bryce Meredig et al. profile →
Combinatorial screening for new materials in unconstrained composition space with machine learning

2014 549 citations Bryce Meredig, James E. Saal et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James E. Saal United States	35	5.3k	2.4k	1.3k	1.1k	803	76	7.4k
Raymundo Arróyave United States	51	5.0k 0.9×	5.1k 2.1×	1.3k 1.0×	1.2k 1.0×	953 1.2×	311	8.8k
Dezhen Xue China	40	5.7k 1.1×	2.3k 1.0×	1.8k 1.4×	800 0.7×	1.6k 2.0×	148	7.5k
Yanjing Su China	47	6.4k 1.2×	3.2k 1.4×	1.9k 1.5×	917 0.8×	887 1.1×	404	9.3k
Cormac Toher United States	37	4.6k 0.9×	3.6k 1.5×	1.6k 1.3×	1.5k 1.3×	659 0.8×	77	8.0k
Chen Shen China	49	3.6k 0.7×	2.3k 1.0×	2.1k 1.7×	997 0.9×	863 1.1×	304	7.3k
Ghanshyam Pilania United States	37	4.8k 0.9×	806 0.3×	1.6k 1.3×	175 0.2×	585 0.7×	103	6.4k
Bryce Meredig United States	27	5.4k 1.0×	1.1k 0.5×	1.5k 1.1×	288 0.3×	818 1.0×	43	6.7k
Huiqiu Deng China	45	4.5k 0.8×	2.1k 0.9×	2.0k 1.5×	696 0.6×	640 0.8×	380	7.9k
Junichiro Shiomi Japan	54	6.6k 1.2×	974 0.4×	1.7k 1.3×	155 0.1×	989 1.2×	261	8.9k
Katsuyo Thornton United States	40	2.9k 0.5×	1.2k 0.5×	3.8k 3.0×	679 0.6×	767 1.0×	159	7.4k

Countries citing papers authored by James E. Saal

Since Specialization

Citations

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

Fields of papers citing papers by James E. Saal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James E. Saal. 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 James E. Saal. The network helps show where James E. Saal may publish in the future.

Co-authorship network of co-authors of James E. Saal

This figure shows the co-authorship network connecting the top 25 collaborators of James E. Saal. A scholar is included among the top collaborators of James E. Saal 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 James E. Saal. James E. Saal 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

Riley, Brian J., et al.. (2025). Glass-Bonded Monazite Waste Forms for Lanthanide and Actinide Immobilization: From Theoretical Design to Scale-Up Production and Characterization. ACS Omega. 10(30). 33524–33535.

Allec, Sarah I., Eric S. Muckley, Nathan S. Johnson, et al.. (2024). A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community. Integrating materials and manufacturing innovation. 13(2). 406–419. 2 indexed citations

Hegde, Vinay I., Sarah I. Allec, Xiaonan Lu, et al.. (2024). Towards informatics-driven design of nuclear waste forms. Digital Discovery. 3(8). 1450–1466. 4 indexed citations

Allec, Sarah I., Xiaonan Lu, Daniel R. Cassar, et al.. (2024). Evaluation of GlassNet for physics‐informed machine learning of glass stability and glass‐forming ability. Journal of the American Ceramic Society. 107(12). 7784–7799. 3 indexed citations

Muckley, Eric S., James E. Saal, Bryce Meredig, Christopher S. Roper, & John H. Martin. (2023). Interpretable models for extrapolation in scientific machine learning. Digital Discovery. 2(5). 1425–1435. 49 indexed citations

Borg, Christopher K. H., Eric S. Muckley, Clara Nyby, et al.. (2023). Quantifying the performance of machine learning models in materials discovery. Digital Discovery. 2(2). 327–338. 25 indexed citations

Hegde, Vinay I., Christopher K. H. Borg, Maxwell Hutchinson, et al.. (2023). Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD. Physical Review Materials. 7(5). 20 indexed citations

Sarker, Suchismita, James E. Saal, Logan Ward, et al.. (2022). Machine learned synthesizability predictions aided by density functional theory. Communications Materials. 3(1). 21 indexed citations

Han, Junsoo, Angela Y. Gerard, Pin Lu, et al.. (2022). Elementally Resolved Dissolution Kinetics of a Ni-Fe-Cr-Mn-Co Multi-Principal Element Alloy in Sulfuric Acid Using AESEC-EIS. Journal of The Electrochemical Society. 169(8). 81507–81507. 8 indexed citations

10.

Shen, Jiahong, et al.. (2022). Reflections on one million compounds in the open quantum materials database (OQMD). Journal of Physics Materials. 5(3). 31001–31001. 30 indexed citations

11.

Nyby, Clara, Xiaolei Guo, James E. Saal, et al.. (2021). Electrochemical metrics for corrosion resistant alloys. Scientific Data. 8(1). 58–58. 76 indexed citations

12.

Han, Junsoo, Xuejie Li, Angela Y. Gerard, et al.. (2021). Potential Dependent Mn Oxidation and Its Role in Passivation of Ni₃₈Fe₂₀Cr₂₂Mn₁₀Co₁₀Multi-Principal Element Alloy Using Multi-Element Resolved Atomic Emission Spectroelectrochemistry. Journal of The Electrochemical Society. 168(5). 51508–51508. 24 indexed citations

13.

Antono, Erin, Nobuyuki Matsuzawa, Julia Ling, et al.. (2020). Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials. The Journal of Physical Chemistry A. 124(40). 8330–8340. 30 indexed citations

14.

Borg, Christopher K. H., Carolina Frey, Tresa M. Pollock, et al.. (2020). Expanded dataset of mechanical properties and observed phases of multi-principal element alloys. Scientific Data. 7(1). 430–430. 100 indexed citations

15.

Gerard, Angela Y., Junsoo Han, Stephen McDonnell, et al.. (2020). Aqueous passivation of multi-principal element alloy Ni38Fe20Cr22Mn10Co10: Unexpected high Cr enrichment within the passive film. Acta Materialia. 198. 121–133. 84 indexed citations

16.

Scully, John R., Angela Y. Gerard, Christopher D. Taylor, et al.. (2020). Controlling the corrosion resistance of multi-principal element alloys. Scripta Materialia. 188. 96–101. 89 indexed citations

17.

McDonnell, Stephen, Daniel K. Schreiber, Angela Y. Gerard, et al.. (2018). Passivation of a corrosion resistant high entropy alloy in non-oxidizing sulfate solutions. Acta Materialia. 164. 362–376. 176 indexed citations

18.

Wang, Dongshu, Maximilian Amsler, Vinay I. Hegde, et al.. (2018). Crystal structure, energetics, and phase stability of strengthening precipitates in Mg alloys: A first-principles study. Acta Materialia. 158. 65–78. 47 indexed citations

19.

Saal, James E., et al.. (2017). Equilibrium high entropy alloy phase stability from experiments and thermodynamic modeling. Scripta Materialia. 146. 5–8. 95 indexed citations

20.

Saal, James E., et al.. (2017). ICME Design of a Castable, Creep-Resistant, Single-Crystal Turbine Alloy. JOM. 69(5). 880–885. 20 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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