Daniel Wigh

483 total citations · 1 hit paper
5 papers, 288 citations indexed

About

Daniel Wigh is a scholar working on Materials Chemistry, Molecular Biology and Organic Chemistry. According to data from OpenAlex, Daniel Wigh has authored 5 papers receiving a total of 288 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Materials Chemistry, 2 papers in Molecular Biology and 1 paper in Organic Chemistry. Recurrent topics in Daniel Wigh's work include Machine Learning in Materials Science (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (1 paper). Daniel Wigh is often cited by papers focused on Machine Learning in Materials Science (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (1 paper). Daniel Wigh collaborates with scholars based in United Kingdom, Singapore and South Sudan. Daniel Wigh's co-authors include Alexei A. Lapkin, Jonathan M. Goodman, Kobi Felton, Gianni Chessari, Rachel Grainger, Christopher N. Johnson, Connor J. Taylor, Alexander Pomberger, Patrick Pasau and Gbemi Oluleye and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Physical Chemistry A and Journal of Chemical Information and Modeling.

In The Last Decade

Daniel Wigh

5 papers receiving 283 citations

Hit Papers

align trajectories

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.

A review of molecular representation in the age of machine learning

2022 207 citations Daniel Wigh, Jonathan M. Goodman et al. Wiley Interdisciplinary Reviews Computational Molecular Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Wigh United Kingdom	3	159	158	90	66	23	5	288
Samuel Boobier United Kingdom	6	157 1.0×	192 1.2×	55 0.6×	35 0.5×	61 2.7×	10	313
Kevin P. Greenman United States	5	228 1.4×	292 1.8×	123 1.4×	79 1.2×	43 1.9×	9	488
Charles J. McGill United States	7	230 1.4×	261 1.7×	136 1.5×	80 1.2×	60 2.6×	10	477
Tara Zepel Canada	4	69 0.4×	172 1.1×	77 0.9×	166 2.5×	28 1.2×	4	352
Valerio Zullo Italy	6	160 1.0×	172 1.1×	125 1.4×	51 0.8×	40 1.7×	13	342
Veronika Chadimová Sweden	5	232 1.5×	211 1.3×	147 1.6×	60 0.9×	20 0.9×	5	369
Arip Asadulaev Russia	3	315 2.0×	252 1.6×	209 2.3×	44 0.7×	16 0.7×	5	418
Tomoyuki Miyao Japan	12	355 2.2×	225 1.4×	197 2.2×	57 0.9×	44 1.9×	64	523
Nikolas Fechner Germany	11	292 1.8×	115 0.7×	218 2.4×	35 0.5×	50 2.2×	21	404
Natalie S. Eyke United States	7	133 0.8×	251 1.6×	68 0.8×	154 2.3×	24 1.0×	8	404

Countries citing papers authored by Daniel Wigh

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Wigh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Wigh

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

All Works

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5 of 5 papers shown

1.

Wigh, Daniel, et al.. (2024). ORDerly: Data Sets and Benchmarks for Chemical Reaction Data. Journal of Chemical Information and Modeling. 64(9). 3790–3798. 13 indexed citations

2.

Taylor, Connor J., Kobi Felton, Daniel Wigh, et al.. (2023). Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS Central Science. 9(5). 957–968. 64 indexed citations

3.

Wigh, Daniel, Matthieu Tissot, Patrick Pasau, Jonathan M. Goodman, & Alexei A. Lapkin. (2023). Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm. The Journal of Physical Chemistry A. 127(11). 2628–2636. 2 indexed citations

4.

Wigh, Daniel, Jonathan M. Goodman, & Alexei A. Lapkin. (2022). A review of molecular representation in the age of machine learning. Wiley Interdisciplinary Reviews Computational Molecular Science. 12(5). 207 indexed citations breakdown →

5.

Oluleye, Gbemi, et al.. (2019). A Framework for Biogas Exploitation in Italian Waste Water Treatment Plants. SHILAP Revista de lepidopterología. 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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