Bryce Meredig

9.5k total citations · 3 hit papers
43 papers, 6.7k citations indexed

About

Bryce Meredig is a scholar working on Materials Chemistry, Catalysis and Electrical and Electronic Engineering. According to data from OpenAlex, Bryce Meredig has authored 43 papers receiving a total of 6.7k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Materials Chemistry, 6 papers in Catalysis and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Bryce Meredig's work include Machine Learning in Materials Science (32 papers), X-ray Diffraction in Crystallography (12 papers) and Electronic and Structural Properties of Oxides (7 papers). Bryce Meredig is often cited by papers focused on Machine Learning in Materials Science (32 papers), X-ray Diffraction in Crystallography (12 papers) and Electronic and Structural Properties of Oxides (7 papers). Bryce Meredig collaborates with scholars based in United States, Canada and Australia. Bryce Meredig's co-authors include Chris Wolverton, James E. Saal, Scott Kirklin, Muratahan Aykol, Alexander Thompson, Jeff W. Doak, Stefan Rühl, Anton O. Oliynyk, Michael W. Gaultois and Taylor D. Sparks and has published in prestigious journals such as The Journal of Chemical Physics, Chemistry of Materials and Physical Review B.

In The Last Decade

Bryce Meredig

43 papers receiving 6.5k 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, Bryce Meredig et al. 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. npj Computational Materials profile →
Combinatorial screening for new materials in unconstrained composition space with machine learning

2014 549 citations Bryce Meredig, Ankit Agrawal et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bryce Meredig United States	27	5.4k	1.5k	1.1k	818	777	43	6.7k
Scott Kirklin United States	17	4.4k 0.8×	1.6k 1.1×	932 0.9×	781 1.0×	511 0.7×	23	5.5k
James E. Saal United States	35	5.3k 1.0×	1.3k 0.9×	2.4k 2.2×	803 1.0×	573 0.7×	76	7.4k
Ohad Levy United States	26	4.5k 0.8×	1.2k 0.8×	894 0.8×	804 1.0×	501 0.6×	56	6.0k
Ghanshyam Pilania United States	37	4.8k 0.9×	1.6k 1.1×	806 0.8×	585 0.7×	930 1.2×	103	6.4k
Wahyu Setyawan United States	27	4.7k 0.9×	1.2k 0.8×	1.1k 1.0×	641 0.8×	232 0.3×	105	5.9k
Muratahan Aykol United States	34	6.1k 1.1×	5.3k 3.6×	1.4k 1.3×	1.1k 1.4×	740 1.0×	70	11.2k
Cormac Toher United States	37	4.6k 0.8×	1.6k 1.1×	3.6k 3.4×	659 0.8×	319 0.4×	77	8.0k
Stephen Dacek United States	15	7.6k 1.4×	4.7k 3.2×	1.4k 1.3×	1.4k 1.7×	566 0.7×	18	11.1k
Logan Ward United States	24	3.5k 0.7×	867 0.6×	930 0.9×	190 0.2×	792 1.0×	77	4.7k
Christopher Wolverton United States	44	6.8k 1.3×	5.6k 3.8×	1.1k 1.1×	1.6k 2.0×	286 0.4×	137	10.5k

Countries citing papers authored by Bryce Meredig

Since Specialization

Citations

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

Fields of papers citing papers by Bryce Meredig

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bryce Meredig

This figure shows the co-authorship network connecting the top 25 collaborators of Bryce Meredig. A scholar is included among the top collaborators of Bryce Meredig 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 Bryce Meredig. Bryce Meredig 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

Kavalsky, Lance, Vinay I. Hegde, Bryce Meredig, & Venkatasubramanian Viswanathan. (2024). A multiobjective closed-loop approach towards autonomous discovery of electrocatalysts for nitrogen reduction. Digital Discovery. 3(5). 999–1010. 11 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

Kavalsky, Lance, Vinay I. Hegde, Eric S. Muckley, et al.. (2023). By how much can closed-loop frameworks accelerate computational materials discovery?. Digital Discovery. 2(4). 1112–1125. 9 indexed citations

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

Kim, Edward, et al.. (2020). Machine-learned metrics for predicting the likelihood of success in materials discovery. npj Computational Materials. 6(1). 24 indexed citations

Viswanathan, Gayatri, Anton O. Oliynyk, Erin Antono, et al.. (2019). Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures. Inorganic Chemistry. 58(14). 9004–9015. 11 indexed citations

Hutchinson, Maxwell, Erin Antono, Brenna M. Gibbons, et al.. (2018). Solving industrial materials problems by using machine learning across diverse computational and experimental data. Bulletin of the American Physical Society. 2018. 4 indexed citations

10.

Wu, Henry, et al.. (2017). Robust FCC solute diffusion predictions from ab-initio machine learning methods. Computational Materials Science. 134. 160–165. 57 indexed citations

11.

Meredig, Bryce. (2017). Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain. Current Opinion in Solid State and Materials Science. 21(3). 159–166. 30 indexed citations

12.

Hart, Gus L. W., Lance J. Nelson, Richard Vanfleet, et al.. (2016). Revisiting the revised Ag-Pt phase diagram. Acta Materialia. 124. 325–332. 28 indexed citations

13.

Michel, Kyle & Bryce Meredig. (2016). Beyond bulk single crystals: A data format for all materials structure–property–processing relationships. MRS Bulletin. 41(8). 617–623. 26 indexed citations

14.

Agrawal, Ankit, Bryce Meredig, Chris Wolverton, & Alok Choudhary. (2016). A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining. 1276–1279. 23 indexed citations

15.

Oliynyk, Anton O., Erin Antono, Taylor D. Sparks, et al.. (2016). High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds. Chemistry of Materials. 28(20). 7324–7331. 277 indexed citations

16.

Gaultois, Michael W., et al.. (2016). Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Materials. 4(5). 150 indexed citations

17.

Sparks, Taylor D., Michael W. Gaultois, Anton O. Oliynyk, Jakoah Brgoch, & Bryce Meredig. (2015). Data mining our way to the next generation of thermoelectrics. Scripta Materialia. 111. 10–15. 102 indexed citations

18.

Thompson, Alexander, Bryce Meredig, & Chris Wolverton. (2014). An improved interatomic potential for xenon in UO₂: a combined density functional theory/genetic algorithm approach. Journal of Physics Condensed Matter. 26(10). 105501–105501. 9 indexed citations

19.

Meredig, Bryce & Chris Wolverton. (2014). Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends. Chemistry of Materials. 26(6). 1985–1991. 39 indexed citations

20.

Meredig, Bryce. (2012). Data-Driven Computational Methods for Materials Characterization, Classification, and Discovery. PhDT. 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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