Nicola Molinari
- Materials Chemistry top 5%
- Electrical and Electronic Engineering top 5%
- Computational Theory and Mathematics top 2%
- Catalysis top 5%
- Molecular Biology
- Co-authors
- Boris KozinskyJonathan P. MailoaAlbert MusaelianLixin SunMordechai KornbluthSimon BatznerTess SmidtMario Geiger
- Topics
- Advanced Battery Materials and Technologies (7 papers)Ionic liquids properties and applications (7 papers)Machine Learning in Materials Science (5 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of the American Chemical SocietyPhysical Review Letters
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Nicola Molinari
23 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Materials Chemistry 1.0k
- Electrical and Electronic Engineering 653
- Computational Theory and Mathematics 287
- Catalysis 248
- Molecular Biology 212
Countries citing papers authored by Nicola Molinari
This map shows the geographic impact of Nicola Molinari'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 Nicola Molinari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Molinari more than expected).
Fields of papers citing papers by Nicola Molinari
This network shows the impact of papers produced by Nicola Molinari. 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 Nicola Molinari. The network helps show where Nicola Molinari may publish in the future.
Co-authorship network of co-authors of Nicola Molinari
This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Molinari. A scholar is included among the top collaborators of Nicola Molinari 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 Nicola Molinari. Nicola Molinari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 5 | |
| 3 | 9 | |
| 4 | 20 | |
| 5 | 12 | |
| 6 | 13 | |
| 7 | E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsbreakdown → | 1022 |
| 8 | 8 | |
| 9 | 32 | |
| 10 | 1 | |
| 11 | 57 | |
| 12 | 29 | |
| 13 | 61 | |
| 14 | 34 | |
| 15 | 86 | |
| 16 | 11 | |
| 17 | 64 | |
| 18 | 232 | |
| 19 | 18 | |
| 20 | An atomistic model for cross-linked HNBR elastomers used in seals | 2 |
About Nicola Molinari
Nicola Molinari is a scholar working on Catalysis, Filtration and Separation and Polymers and Plastics, having authored 23 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Battery Materials and Technologies (7 papers), Ionic liquids properties and applications (7 papers) and Machine Learning in Materials Science (5 papers). The work is most often cited by research in Catalysis (248 citations), Materials Chemistry (1.0k citations) and Computational Theory and Mathematics (287 citations). Nicola Molinari has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Boris Kozinsky, Jonathan P. Mailoa, Albert Musaelian, Lixin Sun, Mordechai Kornbluth, Simon Batzner, Tess Smidt, Mario Geiger, Adrian P. Sutton and Arash A. Mostofi. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.
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.