Edward J. Beard
- Materials Chemistry
- Computational Theory and Mathematics top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Renewable Energy, Sustainability and the Environment
- Co-authors
- Jacqueline M. ColeÁlvaro Vázquez‐MayagoitiaGanesh SivaramanVenkatram VishwanathC. B. CooperGovardhana Babu BodedlaXunjin ZhuGavin B. G. Stenning
- Topics
- Machine Learning in Materials Science (5 papers)Computational Drug Discovery Methods (3 papers)Advanced Photocatalysis Techniques (2 papers)
- Partner nations
- United KingdomUnited StatesHong Kong
In The Last Decade
Edward J. Beard
8 papers receiving 292 citations
Peers
Comparison fields: 5 of 82
- Materials Chemistry 184
- Computational Theory and Mathematics 74
- Artificial Intelligence 43
- Electrical and Electronic Engineering 43
- Renewable Energy, Sustainability and the Environment 35
Countries citing papers authored by Edward J. Beard
This map shows the geographic impact of Edward J. Beard'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 Edward J. Beard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward J. Beard more than expected).
Fields of papers citing papers by Edward J. Beard
This network shows the impact of papers produced by Edward J. Beard. 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 Edward J. Beard. The network helps show where Edward J. Beard may publish in the future.
Co-authorship network of co-authors of Edward J. Beard
This figure shows the co-authorship network connecting the top 25 collaborators of Edward J. Beard. A scholar is included among the top collaborators of Edward J. Beard 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 Edward J. Beard. Edward J. Beard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 43 | |
| 2 | 46 | |
| 3 | 32 | |
| 4 | UV/vis absorption spectra database auto-generated for optical applications via the Argonne data science program | 1 |
| 5 | 74 | |
| 6 | 41 | |
| 7 | 8 | |
| 8 | 50 |
About Edward J. Beard
Edward J. Beard is a scholar working on Industrial and Manufacturing Engineering, Computational Theory and Mathematics and Renewable Energy, Sustainability and the Environment, having authored 8 papers that have together received 295 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (5 papers), Computational Drug Discovery Methods (3 papers) and Advanced Photocatalysis Techniques (2 papers). The work is most often cited by research in Computational Theory and Mathematics (74 citations), Materials Chemistry (184 citations) and Health Informatics (5 citations). Edward J. Beard has collaborated with scholars based in United Kingdom, United States and Hong Kong. Frequent co-authors include Jacqueline M. Cole, Álvaro Vázquez‐Mayagoitia, Ganesh Sivaraman, Venkatram Vishwanath, C. B. Cooper, Govardhana Babu Bodedla, Xunjin Zhu, Gavin B. G. Stenning, Liliana Stan and Santiago Franco. Their work appears in journals such as PLoS ONE, Advanced Energy Materials and Journal of Chemical Information and Modeling.
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.