Gianmarco Terrones

569 total citations
18 papers, 354 citations indexed

About

Gianmarco Terrones is a scholar working on Materials Chemistry, Inorganic Chemistry and Organic Chemistry. According to data from OpenAlex, Gianmarco Terrones has authored 18 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Materials Chemistry, 8 papers in Inorganic Chemistry and 5 papers in Organic Chemistry. Recurrent topics in Gianmarco Terrones's work include Machine Learning in Materials Science (11 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Catalytic C–H Functionalization Methods (3 papers). Gianmarco Terrones is often cited by papers focused on Machine Learning in Materials Science (11 papers), Metal-Organic Frameworks: Synthesis and Applications (8 papers) and Catalytic C–H Functionalization Methods (3 papers). Gianmarco Terrones collaborates with scholars based in United States, Canada and South Korea. Gianmarco Terrones's co-authors include Heather J. Kulik, Aditya Nandy, Chenru Duan, David W. Kastner, Shuwen Yue, Yongchul G. Chung, Matthew P. Rivera, Corinna S. Schindler, Emily R. Wearing and Ilia Kevlishvili and has published in prestigious journals such as Science, Journal of the American Chemical Society and Angewandte Chemie International Edition.

In The Last Decade

Gianmarco Terrones

18 papers receiving 347 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gianmarco Terrones United States 9 217 190 58 34 34 18 354
Niko Prasetyo Indonesia 11 136 0.6× 65 0.3× 33 0.6× 9 0.3× 30 0.9× 39 298
Reisel Millán Spain 10 260 1.2× 154 0.8× 134 2.3× 21 0.6× 25 0.7× 16 374
H. Ray Kelly United States 7 143 0.7× 37 0.2× 40 0.7× 46 1.4× 55 1.6× 17 284
Ludwig Schwiedrzik Austria 6 323 1.5× 83 0.4× 15 0.3× 95 2.8× 44 1.3× 6 380
Saqib Ali Pakistan 13 97 0.4× 124 0.7× 170 2.9× 10 0.3× 40 1.2× 33 351
Nicholas Lease United States 13 183 0.8× 136 0.7× 346 6.0× 22 0.6× 10 0.3× 29 600
Midhun Mohan India 10 189 0.9× 85 0.4× 43 0.7× 9 0.3× 99 2.9× 26 368
Dalar Nazarian United States 3 220 1.0× 254 1.3× 11 0.2× 8 0.2× 22 0.6× 3 316
Jun Yuan China 10 198 0.9× 69 0.4× 218 3.8× 6 0.2× 35 1.0× 25 431
Ashish K. Asatkar India 11 200 0.9× 50 0.3× 114 2.0× 7 0.2× 38 1.1× 20 356

Countries citing papers authored by Gianmarco Terrones

Since Specialization
Citations

This map shows the geographic impact of Gianmarco Terrones'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 Gianmarco Terrones with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianmarco Terrones more than expected).

Fields of papers citing papers by Gianmarco Terrones

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gianmarco Terrones. 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 Gianmarco Terrones. The network helps show where Gianmarco Terrones may publish in the future.

Co-authorship network of co-authors of Gianmarco Terrones

This figure shows the co-authorship network connecting the top 25 collaborators of Gianmarco Terrones. A scholar is included among the top collaborators of Gianmarco Terrones 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 Gianmarco Terrones. Gianmarco Terrones is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Wearing, Emily R., Gianmarco Terrones, Kaid C. Harper, et al.. (2025). Monocyclic Azetidines via a Visible-Light-Mediated Aza Paternò-Büchi Reaction of Ketone-Derived Sulfonylimines. Journal of the American Chemical Society. 147(33). 29722–29731. 5 indexed citations
2.
Terrones, Gianmarco, et al.. (2025). Data-Driven Discovery of Water-Stable Metal–Organic Frameworks with High Water Uptake Capacity. ACS Applied Materials & Interfaces. 17(24). 35971–35985. 1 indexed citations
3.
Jia, Haojun, Chenru Duan, Gianmarco Terrones, Ilia Kevlishvili, & Heather J. Kulik. (2025). Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction. Journal of Catalysis. 448. 116163–116163. 2 indexed citations
4.
Chheda, Saumil, Ju Huang, Haewon Kim, et al.. (2025). CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening. Matter. 8(6). 102140–102140. 23 indexed citations
5.
Rivera, Matthew P., Gianmarco Terrones, Tae Hoon Lee, Zachary P. Smith, & Heather J. Kulik. (2024). Data-Driven Screening and Discovery of Metal–Organic Frameworks as C 2 Adsorbents from over 900 Experimental Isotherms. ACS Applied Materials & Interfaces. 16(47). 64759–64773. 3 indexed citations
6.
Kevlishvili, Ilia, et al.. (2024). Visible-Light-Mediated Macrocyclization for the Formation of Azetine-Based Dimers. ACS Catalysis. 14(6). 4175–4185. 4 indexed citations
7.
Terrones, Gianmarco, B. Adinarayana, Thomas E. Maher, et al.. (2024). A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes. Angewandte Chemie. 136(18). 2 indexed citations
8.
Wearing, Emily R., Yu‐Cheng Yeh, Gianmarco Terrones, et al.. (2024). Visible light–mediated aza Paternò–Büchi reaction of acyclic oximes and alkenes to azetidines. Science. 384(6703). 1468–1476. 36 indexed citations
9.
Terrones, Gianmarco, B. Adinarayana, Thomas E. Maher, et al.. (2024). A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes. Angewandte Chemie International Edition. 63(18). e202401808–e202401808. 19 indexed citations
10.
Terrones, Gianmarco, et al.. (2024). Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set. Journal of the American Chemical Society. 146(29). 20333–20348. 41 indexed citations
11.
Nandy, Aditya, Shuwen Yue, Chenru Duan, et al.. (2023). A database of ultrastable MOFs reassembled from stable fragments with machine learning models. Matter. 6(5). 1585–1603. 56 indexed citations
12.
Terrones, Gianmarco, Chenru Duan, Aditya Nandy, & Heather J. Kulik. (2023). Low-cost machine learning prediction of excited state properties of iridium-centered phosphors. Chemical Science. 14(6). 1419–1433. 25 indexed citations
13.
Yue, Shuwen, et al.. (2023). Effects of MOF linker rotation and functionalization on methane uptake and diffusion. Molecular Systems Design & Engineering. 8(4). 527–537. 13 indexed citations
14.
Terrones, Gianmarco, et al.. (2023). Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes. The Journal of Physical Chemistry A. 128(1). 204–216. 8 indexed citations
15.
Terrones, Gianmarco, et al.. (2023). SESAMI APP: An Accessible Interface for Surface AreaCalculation of Materials from Adsorption Isotherms. The Journal of Open Source Software. 8(86). 5429–5429. 7 indexed citations
16.
Duan, Chenru, Aditya Nandy, Gianmarco Terrones, David W. Kastner, & Heather J. Kulik. (2022). Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores. JACS Au. 3(2). 391–401. 16 indexed citations
17.
Nandy, Aditya, et al.. (2022). MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks. Scientific Data. 9(1). 74–74. 92 indexed citations
18.
Nandy, Aditya, Shuwen Yue, Chenru Duan, et al.. (2022). A Database of Ultrastable MOFs Reassembled From Stable Fragments With Machine Learning Models. SSRN Electronic Journal. 1 indexed citations

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.

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