Alexander Dunn

3.1k total citations · 3 hit papers
14 papers, 2.0k citations indexed

About

Alexander Dunn is a scholar working on Materials Chemistry, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Alexander Dunn has authored 14 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Materials Chemistry, 4 papers in Artificial Intelligence and 3 papers in Computational Theory and Mathematics. Recurrent topics in Alexander Dunn's work include Machine Learning in Materials Science (9 papers), Topic Modeling (4 papers) and Computational Drug Discovery Methods (3 papers). Alexander Dunn is often cited by papers focused on Machine Learning in Materials Science (9 papers), Topic Modeling (4 papers) and Computational Drug Discovery Methods (3 papers). Alexander Dunn collaborates with scholars based in United States, United Kingdom and Belgium. Alexander Dunn's co-authors include Anubhav Jain, Kristin A. Persson, Gerbrand Ceder, John Dagdelen, Vahe Tshitoyan, Ziqin Rong, Olga Kononova, Leigh Weston, Maxwell Dylla and G. Jeffrey Snyder and has published in prestigious journals such as Nature, Nature Communications and Journal of Applied Physics.

In The Last Decade

Alexander Dunn

14 papers receiving 1.9k citations

Hit Papers

Unsupervised word embeddings capture latent knowledge fro... 2018 2026 2020 2023 2019 2018 2024 200 400 600
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. Unsupervised word embeddings capture latent knowledge from materials science literature
    2019 698 citations Vahe Tshitoyan, John Dagdelen et al. Nature profile →
  2. Matminer: An open source toolkit for materials data mining
    2018 675 citations Logan Ward, Alexander Dunn et al. Computational Materials Science profile →
  3. Structured information extraction from scientific text with large language models
    2024 225 citations John Dagdelen, Alexander Dunn et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Dunn United States 11 1.4k 357 335 304 143 14 2.0k
Vahe Tshitoyan United States 10 954 0.7× 271 0.8× 336 1.0× 248 0.8× 112 0.8× 12 1.5k
John Dagdelen United States 9 1.0k 0.7× 232 0.6× 392 1.2× 221 0.7× 135 0.9× 11 1.7k
Leigh Weston United States 11 1.1k 0.8× 384 1.1× 248 0.7× 158 0.5× 115 0.8× 16 1.5k
Tanjin He United States 19 1.1k 0.8× 315 0.9× 177 0.5× 178 0.6× 232 1.6× 27 1.7k
Kevin Huang United States 17 741 0.5× 240 0.7× 446 1.3× 225 0.7× 196 1.4× 70 1.6k
Mário R. G. Marques Germany 4 1.1k 0.8× 350 1.0× 157 0.5× 215 0.7× 187 1.3× 6 1.8k
Haoyan Huo United States 15 880 0.6× 206 0.6× 232 0.7× 212 0.7× 83 0.6× 18 1.1k
Tian Xie China 17 2.0k 1.5× 679 1.9× 173 0.5× 461 1.5× 125 0.9× 37 2.6k
Christoph Kreisbeck United States 16 844 0.6× 344 1.0× 157 0.5× 269 0.9× 273 1.9× 26 1.7k
Loı̈c M. Roch Switzerland 19 1.3k 0.9× 490 1.4× 123 0.4× 385 1.3× 482 3.4× 36 2.2k

Countries citing papers authored by Alexander Dunn

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Dunn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Dunn

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

All Works

14 of 14 papers shown
1.
Dagdelen, John, Alexander Dunn, Sang‐Hoon Lee, et al.. (2024). Structured information extraction from scientific text with large language models. Nature Communications. 15(1). 1418–1418. 225 indexed citations breakdown →
2.
Walker, Nicholas, Sanghoon Lee, John Dagdelen, et al.. (2023). Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs. Digital Discovery. 2(6). 1768–1782. 14 indexed citations
3.
Trewartha, Amalie, Nicholas Walker, Haoyan Huo, et al.. (2022). Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. Patterns. 3(4). 100488–100488. 108 indexed citations
4.
Huo, Haoyan, Christopher J. Bartel, Tanjin He, et al.. (2022). Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions. Chemistry of Materials. 34(16). 7323–7336. 51 indexed citations
5.
Walker, Nicholas, Amalie Trewartha, Haoyan Huo, et al.. (2021). The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science. SSRN Electronic Journal. 18 indexed citations
6.
Pöhls, Jan‐Hendrik, Sevan Chanakian, Junsoo Park, et al.. (2020). Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculations. Materials Horizons. 8(1). 209–215. 57 indexed citations
7.
Dylla, Maxwell, Alexander Dunn, Shashwat Anand, Anubhav Jain, & G. Jeffrey Snyder. (2020). Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials. Research. 2020. 6375171–6375171. 41 indexed citations
8.
Dunn, Alexander, et al.. (2020). Author Correction: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Computational Materials. 6(1). 10 indexed citations
9.
Ricci, Francesco, Alexander Dunn, Anubhav Jain, Gian‐Marco Rignanese, & Geoffroy Hautier. (2020). Gapped metals as thermoelectric materials revealed by high-throughput screening. Journal of Materials Chemistry A. 8(34). 17579–17594. 28 indexed citations
10.
Tshitoyan, Vahe, John Dagdelen, Leigh Weston, et al.. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 571(7763). 95–98. 698 indexed citations breakdown →
11.
Dunn, Alexander, et al.. (2019). Rocketsled: a software library for optimizing high-throughput computational searches. Journal of Physics Materials. 2(3). 34002–34002. 11 indexed citations
12.
Ward, Logan, Alexander Dunn, Alireza Faghaninia, et al.. (2018). Matminer: An open source toolkit for materials data mining. Computational Materials Science. 152. 60–69. 675 indexed citations breakdown →
13.
Dunn, Alexander & Dorothy M. Duffy. (2011). A molecular dynamics study of diamond and graphite under tritium bombardment. Journal of Applied Physics. 110(10). 8 indexed citations
14.
Dunn, Alexander, Dorothy M. Duffy, & A.M. Stoneham. (2010). A molecular dynamics study of diamond exposed to tritium bombardment for fusion applications. Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms. 269(14). 1724–1726. 10 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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