Joshua Schrier
- Materials Chemistry top 2%
- Electrical and Electronic Engineering top 5%
- Biomedical Engineering top 5%
- Mechanical Engineering top 5%
- Electronic, Optical and Magnetic Materials top 5%
- Co-authors
- Alexander J. NorquistMatthias ZellerSorelle A. FriedlerPhilip AdlerKatherine C. ElbertMalia B. WennyPaul RaccugliaAurelio Mollo
- Topics
- Machine Learning in Materials Science (22 papers)Crystal Structures and Properties (12 papers)Quantum Dots Synthesis And Properties (12 papers)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Joshua Schrier
87 papers receiving 4.7k citations
Hit Papers
Peers
Comparison fields: 5 of 155
- Materials Chemistry 2.8k
- Electrical and Electronic Engineering 1.3k
- Biomedical Engineering 842
- Mechanical Engineering 646
- Electronic, Optical and Magnetic Materials 396
Countries citing papers authored by Joshua Schrier
This map shows the geographic impact of Joshua Schrier'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 Joshua Schrier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua Schrier more than expected).
Fields of papers citing papers by Joshua Schrier
This network shows the impact of papers produced by Joshua Schrier. 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 Joshua Schrier. The network helps show where Joshua Schrier may publish in the future.
Co-authorship network of co-authors of Joshua Schrier
This figure shows the co-authorship network connecting the top 25 collaborators of Joshua Schrier. A scholar is included among the top collaborators of Joshua Schrier 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 Joshua Schrier. Joshua Schrier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 28 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 3 | |
| 9 | 22 | |
| 10 | 53 | |
| 11 | 22 | |
| 12 | 7 | |
| 13 | 135 | |
| 14 | 26 | |
| 15 | 11 | |
| 16 | 35 | |
| 17 | Machine-learning-assisted materials discovery using failed experimentsbreakdown → | 1251 |
| 18 | 8 | |
| 19 | 24 | |
| 20 | 319 |
About Joshua Schrier
Joshua Schrier is a scholar working on Physical and Theoretical Chemistry, Catalysis and Inorganic Chemistry, having authored 90 papers that have together received 4.8k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (22 papers), Crystal Structures and Properties (12 papers) and Quantum Dots Synthesis And Properties (12 papers). The work is most often cited by research in Materials Chemistry (2.8k citations), Catalysis (214 citations) and Water Science and Technology (368 citations). Joshua Schrier has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Alexander J. Norquist, Matthias Zeller, Sorelle A. Friedler, Philip Adler, Katherine C. Elbert, Malia B. Wenny, Paul Raccuglia, Aurelio Mollo, A. Paul Alivisatos and D. O. Demchenko. Their work appears in journals such as Nature, Journal of the American Chemical Society and Angewandte Chemie International Edition.
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.