Anna M. Hiszpanski

1.7k total citations · 1 hit paper
35 papers, 1.3k citations indexed

About

Anna M. Hiszpanski is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Anna M. Hiszpanski has authored 35 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Materials Chemistry, 15 papers in Electrical and Electronic Engineering and 7 papers in Computational Theory and Mathematics. Recurrent topics in Anna M. Hiszpanski's work include Machine Learning in Materials Science (10 papers), Organic Electronics and Photovoltaics (10 papers) and Computational Drug Discovery Methods (7 papers). Anna M. Hiszpanski is often cited by papers focused on Machine Learning in Materials Science (10 papers), Organic Electronics and Photovoltaics (10 papers) and Computational Drug Discovery Methods (7 papers). Anna M. Hiszpanski collaborates with scholars based in United States, Australia and South Korea. Anna M. Hiszpanski's co-authors include Yueh‐Lin Loo, T. Yong-Jin Han, Bhavya Kailkhura, Brian Gallagher, Colin Nuckolls, Shusen Liu, Xiaoting Zhong, Gerbrand Ceder, Edward Kim and Jacqueline M. Cole and has published in prestigious journals such as Journal of the American Chemical Society, Nano Letters and ACS Nano.

In The Last Decade

Anna M. Hiszpanski

34 papers receiving 1.3k citations

Hit Papers

Explainable machine learn... 2022 2026 2023 2024 2022 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Explainable machine learning in materials science
    2022 214 citations Xiaoting Zhong, Brian Gallagher et al. npj Computational Materials profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anna M. Hiszpanski United States 19 740 529 211 145 143 35 1.3k
Katherine C. Elbert United States 10 1.2k 1.6× 609 1.2× 149 0.7× 160 1.1× 195 1.4× 16 1.9k
Daniel P. Tabor United States 23 586 0.8× 1.8k 3.4× 192 0.9× 297 2.0× 84 0.6× 47 2.6k
A. Gilad Kusne United States 18 1.2k 1.7× 644 1.2× 193 0.9× 127 0.9× 25 0.2× 37 1.9k
Tarak K. Patra India 16 609 0.8× 149 0.3× 236 1.1× 48 0.3× 173 1.2× 50 992
Venkatesh Botu United States 12 1.1k 1.5× 367 0.7× 67 0.3× 61 0.4× 54 0.4× 15 1.5k
Dequan Li China 25 592 0.8× 601 1.1× 77 0.4× 208 1.4× 140 1.0× 146 1.8k
Laurent Simon France 28 1.3k 1.8× 690 1.3× 96 0.5× 112 0.8× 238 1.7× 112 2.4k
Xiaolin Hu China 20 560 0.8× 589 1.1× 50 0.2× 313 2.2× 68 0.5× 84 1.2k

Countries citing papers authored by Anna M. Hiszpanski

Since Specialization
Citations

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

Fields of papers citing papers by Anna M. Hiszpanski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna M. Hiszpanski. 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 Anna M. Hiszpanski. The network helps show where Anna M. Hiszpanski may publish in the future.

Co-authorship network of co-authors of Anna M. Hiszpanski

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

All Works

20 of 20 papers shown
1.
Choi, Jiwoo, Jaewoong Choi, Kihoon Bang, et al.. (2025). MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature. Advanced Science. 12(16). e2408221–e2408221. 4 indexed citations
2.
3.
Tom, Rithwik, et al.. (2023). Ab Initio Crystal Structure Prediction of the Energetic Materials LLM-105, RDX, and HMX. Crystal Growth & Design. 23(9). 6275–6289. 8 indexed citations
4.
Choi, Jiwoo, Kihoon Bang, Jaewoong Choi, et al.. (2023). Deep learning of electrochemical CO2 conversion literature reveals research trends and directions. Journal of Materials Chemistry A. 11(33). 17628–17643. 9 indexed citations
5.
Zhong, Xiaoting, Brian Gallagher, Shusen Liu, et al.. (2022). Explainable machine learning in materials science. npj Computational Materials. 8(1). 214 indexed citations breakdown →
6.
Liu, Shusen, Bhavya Kailkhura, Jize Zhang, et al.. (2022). Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis. ACS Omega. 7(3). 2624–2637. 4 indexed citations
7.
Antoniuk, Evan R., Peggy Li, Bhavya Kailkhura, & Anna M. Hiszpanski. (2022). Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions. Journal of Chemical Information and Modeling. 62(22). 5435–5445. 43 indexed citations
8.
Loveland, Donald, et al.. (2021). Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning. Journal of Chemical Information and Modeling. 61(5). 2147–2158. 52 indexed citations
9.
Nakotte, Tom, John W. Murphy, Steven A. Hawks, et al.. (2021). Colloidal quantum dot based infrared detectors: extending to the mid-infrared and moving from the lab to the field. Journal of Materials Chemistry C. 10(3). 790–804. 33 indexed citations
10.
Hiszpanski, Anna M., Brian Gallagher, Peggy Li, et al.. (2020). Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. Journal of Chemical Information and Modeling. 60(6). 2876–2887. 52 indexed citations
11.
Olivetti, Elsa, Jacqueline M. Cole, Edward Kim, et al.. (2020). Data-driven materials research enabled by natural language processing and information extraction. Applied Physics Reviews. 7(4). 198 indexed citations
12.
Hiszpanski, Anna M., et al.. (2018). Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives. Chemistry of Materials. 30(10). 3330–3337. 10 indexed citations
13.
Hiszpanski, Anna M., Arthur R. Woll, Bumjung Kim, Colin Nuckolls, & Yueh‐Lin Loo. (2017). Altering the Polymorphic Accessibility of Polycyclic Aromatic Hydrocarbons with Fluorination. Chemistry of Materials. 29(10). 4311–4316. 17 indexed citations
14.
Campbell, Patrick G., Marcus A. Worsley, Anna M. Hiszpanski, Theodore F. Baumann, & Juergen Biener. (2015). Synthesis and Functionalization of 3D Nano-graphene Materials: Graphene Aerogels and Graphene Macro Assemblies. Journal of Visualized Experiments. e53235–e53235. 3 indexed citations
15.
Hiszpanski, Anna M., Melda Sezen-Edmonds, Tal Galfsky, et al.. (2015). Metal nanocluster light-emitting devices with suppressed parasitic emission and improved efficiency: exploring the impact of photophysical properties. Nanoscale. 7(20). 9140–9146. 39 indexed citations
16.
Hiszpanski, Anna M., Leo Shaw, He Wang, et al.. (2015). Halogenation of a Nonplanar Molecular Semiconductor to Tune Energy Levels and Bandgaps for Electron Transport. Chemistry of Materials. 27(5). 1892–1900. 56 indexed citations
17.
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Kang, Seok Ju, Seokhoon Ahn, Jong Bok Kim, et al.. (2013). Using Self-Organization To Control Morphology in Molecular Photovoltaics. Journal of the American Chemical Society. 135(6). 2207–2212. 129 indexed citations
19.
Hiszpanski, Anna M. & Yueh‐Lin Loo. (2013). Directing the film structure of organic semiconductors via post-deposition processing for transistor and solar cell applications. Energy & Environmental Science. 7(2). 592–608. 68 indexed citations
20.
Kang, Seok Ju, Seokhoon Ahn, Jong Bok Kim, et al.. (2013). Correction to “Using Self-Organization To Control Morphology in Molecular Photovoltaics”. Journal of the American Chemical Society. 135(28). 10579–10579. 2 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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