David J. Wales

29.2k total citations · 3 hit papers
477 papers, 22.5k citations indexed

About

David J. Wales is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, David J. Wales has authored 477 papers receiving a total of 22.5k indexed citations (citations by other indexed papers that have themselves been cited), including 224 papers in Atomic and Molecular Physics, and Optics, 192 papers in Materials Chemistry and 141 papers in Molecular Biology. Recurrent topics in David J. Wales's work include Advanced Chemical Physics Studies (169 papers), Protein Structure and Dynamics (106 papers) and Spectroscopy and Quantum Chemical Studies (97 papers). David J. Wales is often cited by papers focused on Advanced Chemical Physics Studies (169 papers), Protein Structure and Dynamics (106 papers) and Spectroscopy and Quantum Chemical Studies (97 papers). David J. Wales collaborates with scholars based in United Kingdom, United States and Germany. David J. Wales's co-authors include Jonathan P. K. Doye, Anthony J. Stone, Harold A. Scheraga, R. Stephen Berry, Mark A. Miller, Tiffany R. Walsh, Matthew P. Hodges, Lindsey J. Munro, David A. Evans and J. Hernández‐Rojas and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

David J. Wales

463 papers receiving 21.0k 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.

Theoretical studies of icosahedral C60 and some related species

1986 1.5k citations David J. Wales et al. profile →
Global Optimization of Clusters, Crystals, and Biomolecules

1999 886 citations David J. Wales et al. profile →
Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

2016 339 citations Thomas Desautels, Jacob Calvert et al. JMIR Medical Informatics profile →

Peers

Countries citing papers authored by David J. Wales

Since Specialization

Citations

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

Fields of papers citing papers by David J. Wales

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Wales. 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 David J. Wales. The network helps show where David J. Wales may publish in the future.

Co-authorship network of co-authors of David J. Wales

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	A pseudo-cubic metal–organic cage with conformationally switchable faces for dynamically adaptive guest encapsulation 2025 ·Nature Chemistry ·(unknown), Tanya K. Ronson, Andrew W. Heard, (unknown), Laura Schneider, Philipp Pracht, John D. Thoburn, David J. Wales, Jonathan R. Nitschke	14
2	Benchmark of Available Explicit Solvent Models in CHARMM36m to Characterize Glycosaminoglycans 2025 ·The Journal of Physical Chemistry B ·Patryk A. Wesołowski, David J. Wales, (unknown)	0
3	Synthesis of covalently linked knotted cage frameworks 2025 ·Nature Synthesis ·(unknown), Tanya K. Ronson, (unknown), (unknown), Andrew W. Heard, Paola Posocco, David J. Wales, Jonathan R. Nitschke	1
4	The energy landscape of Aβ₄₂: a funnel to disorder for the monomer becomes a folding funnel for self-assembly 2024 ·Chemical Communications ·(unknown), David J. Wales, Birgit Strodel	2
5	Design principles for energy transfer in the photosystem II supercomplex from kinetic transition networks 2024 ·Nature Communications ·(unknown), David J. Wales, (unknown), Graham R. Fleming	6
6	Going for Gold(-Standard): Attaining Coupled Cluster Accuracy in Oxide-Supported Nanoclusters 2024 ·Journal of Chemical Theory and Computation ·(unknown), David J. Wales, Angelos Michaelides, Chang Woo Myung	2
7	Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML) 2024 ·Digital Discovery ·(unknown), (unknown), Edward O. Pyzer‐Knapp, David J. Wales	3
8	Exploring energy landscapes for solid-state systems with variable cells at the extended tight-binding level 2023 ·The Journal of Chemical Physics ·Philipp Pracht, (unknown), David J. Wales	6
9	Energy landscapes and heat capacity signatures for peptides correlate with phase separation propensity 2023 ·SHILAP Revista de lepidopterología ·(unknown), Rosana Collepardo‐Guevara, Jerelle A. Joseph, David J. Wales	4
10	On the capacity and superposition of minima in neural network loss function landscapes 2022 ·Machine Learning Science and Technology ·(unknown), (unknown), (unknown), David J. Wales	4
11	Crystal Structure Prediction for Benzene Using Basin-Hopping Global Optimization 2021 ·The Journal of Physical Chemistry A ·(unknown), (unknown), Samuel P. Niblett, David J. Wales	17
12	Side-Chain Polarity Modulates the Intrinsic Conformational Landscape of Model Dipeptides 2021 ·The Journal of Physical Chemistry B ·Debayan Chakraborty, (unknown), David J. Wales	5
13	Fragility and correlated dynamics in supercooled liquids 2020 ·The Journal of Chemical Physics ·(unknown), David J. Wales	4
14	Intrinsically Disordered Landscapes for Human CD4 Receptor Peptide 2018 ·The Journal of Physical Chemistry B ·Jerelle A. Joseph, David J. Wales	11
15	Exploring biomolecular energy landscapes 2017 ·Chemical Communications ·Jerelle A. Joseph, Konstantin Röder, Debayan Chakraborty, (unknown), David J. Wales	72
16	Energy landscapes for a machine learning application to series data 2016 ·The Journal of Chemical Physics ·Andrew J. Ballard, Jacob D. Stevenson, Ritankar Das, David J. Wales	19
17	Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach breakdown → 2016 ·JMIR Medical Informatics ·Thomas Desautels, Jacob Calvert, Jana Hoffman, Melissa Jay, Yaniv Kerem, Lisa Shieh, David Shimabukuro, Uli K. Chettipally, Mitchell D. Feldman, Chris W. Barton, David J. Wales, Ritankar Das	339
18	Rate constants, timescales, and free energy barriers 2015 ·Journal of Non-Equilibrium Thermodynamics ·Peter Salamon, David J. Wales, Anca M. Segall, (unknown), J. Christian Schön, Karl Heinz Hoffmann, Bjarne Andresen	10
19	Molecule-doped rare gas clusters: structure and stability of Ar n NO( X ² Π _1/2/3/2 ), n ≤ 25, from new ab initio potential energy surfaces of ArNO 2000 ·Molecular Physics ·Fedor Y. Naumkin, David J. Wales	10
20	Rydberg excitations in rare gas clusters: structure and electronic spectra of Ar* _n (3 ≤ n ≤ 25) 1999 ·Molecular Physics ·Fedor Y. Naumkin, David J. Wales	21

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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