Brian DeCost
Impact in
- Metals and Alloys top 5%
- Structural Biology top 5%
Papers in
-
- Machine Learning in Materials Science 33
- X-ray Diffraction in Crystallography 11
- Electronic and Structural Properties of Oxides 6
- Corrosion Behavior and Inhibition 3
-
- High Entropy Alloys Studies 5
- Co-authors
- Kamal Choudhary (17 shared papers)Elizabeth A. Holm (10 shared papers)Francesca Tavazza (6 shared papers)Toby Francis (2 shared papers)Ankit Agrawal (2 shared papers)Alok Choudhary (2 shared papers)Simon J. L. Billinge (2 shared papers)Anubhav Jain (1 shared paper)
- Journals
- npj Computational Materials (6 papers)Matter (3 papers)JOM (2 papers)Communications Materials (2 papers)Computational Materials Science (2 papers)
- Partner nations
- United StatesCanadaEgypt
In The Last Decade
Brian DeCost
48 papers receiving 3.1k citations
Brian DeCost's Hit Papers
Peers
Comparison fields: 5 of 143
- Metals and Alloys 130
- Structural Biology 64
- Materials Chemistry 2.0k
- Computational Theory and Mathematics 361
- Surfaces, Coatings and Films 139
Countries citing papers authored by Brian DeCost
This map shows the geographic impact of Brian DeCost'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 Brian DeCost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian DeCost more than expected).
Fields of papers citing papers by Brian DeCost
This network shows the impact of papers produced by Brian DeCost. 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 Brian DeCost. The network helps show where Brian DeCost may publish in the future.
Co-authors
The 25 scholars most cited alongside Brian DeCost, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Recent advances and applications of deep learning methods in materials science Hit paper breakdown → | 2022 | 724 |
| 2 | Atomistic Line Graph Neural Network for improved materials property predictions Hit paper breakdown → | 2021 | 459 |
| 3 | 2019 | 263 | |
| 4 | 2019 | 247 | |
| 5 | 2015 | 230 | |
| 6 | 2017 | 171 | |
| 7 | 2018 | 126 | |
| 8 | 2016 | 108 | |
| 9 | 2023 | 77 | |
| 10 | 2023 | 76 | |
| 11 | 2017 | 67 | |
| 12 | 2023 | 66 | |
| 13 | 2017 | 65 | |
| 14 | 2024 | 53 | |
| 15 | 2016 | 47 | |
| 16 | 2022 | 34 | |
| 17 | 2016 | 28 | |
| 18 | 2017 | 27 | |
| 19 | 2023 | 25 | |
| 20 | 2024 | 25 |
About Brian DeCost
Brian DeCost is a scholar working on Materials Chemistry, Mechanical Engineering, Computational Theory and Mathematics, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 50 papers that have together received 3.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (33 papers), X-ray Diffraction in Crystallography (11 papers), Computational Drug Discovery Methods (10 papers), Electronic and Structural Properties of Oxides (6 papers), High Entropy Alloys Studies (5 papers), High-Temperature Coating Behaviors (3 papers), Corrosion Behavior and Inhibition (3 papers) and Electron and X-Ray Spectroscopy Techniques (3 papers). The work is most often cited by research in Metals and Alloys (130 citations), Structural Biology (64 citations), Materials Chemistry (2.0k citations), Computational Theory and Mathematics (361 citations) and Surfaces, Coatings and Films (139 citations). Brian DeCost has collaborated with scholars based in United States, Canada and Egypt. Frequent co-authors include Kamal Choudhary, Elizabeth A. Holm, Francesca Tavazza, Toby Francis, Ankit Agrawal, Alok Choudhary, Simon J. L. Billinge, Anubhav Jain, Shyue Ping Ong and Ryan Cohn. Their work appears in journals such as npj Computational Materials, Matter, JOM, Communications Materials and Computational Materials Science.
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.