Aurelio Mollo
Impact in
- Materials Chemistry top 5%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
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- Computational Drug Discovery Methods
Papers in
-
- RNA and protein synthesis mechanisms 3
- Chemical Synthesis and Analysis 2
- Ecology 3
- Bacteriophages and microbial interactions 3
- Co-authors
- Philip Adler (1 shared paper)Katherine C. Elbert (1 shared paper)Joshua Schrier (1 shared paper)Sorelle A. Friedler (1 shared paper)Paul Raccuglia (1 shared paper)Matthias Zeller (1 shared paper)Malia B. Wenny (1 shared paper)Alexander J. Norquist (1 shared paper)
- Journals
- Journal of the American Chemical Society (1 paper)RSC Advances (1 paper)mBio (1 paper)Nature (1 paper)Chemical Reviews (1 paper)
- Partner nations
- United StatesGermanyAustralia
In The Last Decade
Aurelio Mollo
7 papers receiving 1.4k citations
Aurelio Mollo's Hit Papers
Peers
Comparison fields: 5 of 118
- Materials Chemistry 894
- Computational Theory and Mathematics 198
- Metals and Alloys 29
- Catalysis 66
- Inorganic Chemistry 97
Countries citing papers authored by Aurelio Mollo
This map shows the geographic impact of Aurelio Mollo'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 Aurelio Mollo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurelio Mollo more than expected).
Fields of papers citing papers by Aurelio Mollo
This network shows the impact of papers produced by Aurelio Mollo. 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 Aurelio Mollo. The network helps show where Aurelio Mollo may publish in the future.
Co-authors
The 16 scholars most cited alongside Aurelio Mollo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine-learning-assisted materials discovery using failed experiments Hit paper breakdown → | 2016 | 1288 |
| 2 | 2022 | 65 | |
| 3 | 2016 | 37 | |
| 4 | 2020 | 21 | |
| 5 | 2017 | 14 | |
| 6 | 2016 | 5 | |
| 7 | 2023 | 1 |
About Aurelio Mollo
Aurelio Mollo is a scholar working on Molecular Biology, Ecology, Genetics, Pharmacology and Oncology, having authored 7 papers that have together received 1.4k indexed citations. Recurring topics across this work include Bacterial Genetics and Biotechnology (3 papers), Bacteriophages and microbial interactions (3 papers), RNA and protein synthesis mechanisms (3 papers), Chemical Synthesis and Analysis (2 papers), Microbial Natural Products and Biosynthesis (2 papers), Machine Learning in Materials Science (1 paper), X-ray Diffraction in Crystallography (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Materials Chemistry (894 citations), Computational Theory and Mathematics (198 citations), Metals and Alloys (29 citations), Catalysis (66 citations) and Inorganic Chemistry (97 citations). Aurelio Mollo has collaborated with scholars based in United States, Germany and Australia. Frequent co-authors include Philip Adler, Katherine C. Elbert, Joshua Schrier, Sorelle A. Friedler, Paul Raccuglia, Matthias Zeller, Malia B. Wenny, Alexander J. Norquist, Daniel Kahne and Natividad Ruiz. Their work appears in journals such as Journal of the American Chemical Society, RSC Advances, mBio, Nature and Chemical Reviews.
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.