James L. McDonagh

1.2k total citations · 1 hit paper
24 papers, 886 citations indexed

About

James L. McDonagh is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, James L. McDonagh has authored 24 papers receiving a total of 886 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Materials Chemistry, 6 papers in Molecular Biology and 6 papers in Computational Theory and Mathematics. Recurrent topics in James L. McDonagh's work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (6 papers) and Advanced Chemical Physics Studies (5 papers). James L. McDonagh is often cited by papers focused on Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (6 papers) and Advanced Chemical Physics Studies (5 papers). James L. McDonagh collaborates with scholars based in United Kingdom, United States and Ireland. James L. McDonagh's co-authors include John B. O. Mitchell, Tanja van Mourik, Colin R. Groom, R. Skyner, Mark A. Vincent, Paul L. A. Popelier, David Scott Palmer, Arnaldo F. Silva, Neetika Nath and Maxim V. Fedorov and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry B and Chemical Physics Letters.

In The Last Decade

James L. McDonagh

24 papers receiving 868 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 methods for the calculation of solution free energies and the modelling of systems in solution

2015 406 citations R. Skyner, James L. McDonagh et al. Physical Chemistry Chemical Physics profile →

Peers

James L. McDonagh

CTM

AMPO

PTC

Rubén Laplaza Switzerland

Jonny Proppe Germany

Yanfei Guan United States

Jiří Hostaš Canada

Roberto A. Boto Spain

Samuel T. Chill United States

N. Sukumar United States

Tanmoy Chakraborty India

Steven V. Jerome United States

Geoffrey P. F. Wood United States

Rubén Laplaza Switzerland

James L. McDonagh

410 ×1.0

305 ×1.2

188 ×0.9

CTM

101 ×0.5

AMPO

124 ×0.7

PTC

Citations per year, relative to James L. McDonagh James L. McDonagh (= 1×) peers Rubén Laplaza

Countries citing papers authored by James L. McDonagh

Since Specialization

Citations

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

Fields of papers citing papers by James L. McDonagh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James L. McDonagh. 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 James L. McDonagh. The network helps show where James L. McDonagh may publish in the future.

Co-authorship network of co-authors of James L. McDonagh

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

All Works

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20 of 20 papers shown

McDonagh, James L., et al.. (2024). Chemical space analysis and property prediction for carbon capture solvent molecules. Digital Discovery. 3(3). 528–543. 2 indexed citations

Luan, Binquan & James L. McDonagh. (2024). Developing semi-empirical water model for efficiently simulating temperature-dependent chemisorption of CO₂ in amine solvents. Physical Chemistry Chemical Physics. 26(4). 3540–3547. 1 indexed citations

Subramanian, Vigneshwari, Andrew Baxter, Ola Engkvist, et al.. (2023). Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models. Journal of Chemical Information and Modeling. 63(4). 1099–1113. 14 indexed citations

Elmegreen, Bruce G., Hendrik F. Hamann, Benjamin H. Wunsch, et al.. (2023). MDLab: AI frameworks for carbon capture and battery materials. Frontiers in Environmental Science. 11. 3 indexed citations

Patti, Alessandro, et al.. (2023). Application of machine-learning algorithms to predict the transport properties of Mie fluids. The Journal of Chemical Physics. 159(2). 2 indexed citations

McDonagh, James L., William C. Swope, R. L. Anderson, Michael A. Johnston, & David J. Bray. (2020). What can digitisation do for formulated product innovation and development?. Polymer International. 70(3). 248–255. 12 indexed citations

McDonagh, James L., et al.. (2019). Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields. Journal of Chemical Information and Modeling. 59(10). 4278–4288. 39 indexed citations

McDonagh, James L., Arnaldo F. Silva, Mark A. Vincent, & Paul L. A. Popelier. (2017). Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms. Journal of Chemical Theory and Computation. 14(1). 216–224. 32 indexed citations

Silva, Arnaldo F., Mark A. Vincent, James L. McDonagh, & Paul L. A. Popelier. (2017). The Transferability of Topologically Partitioned Electron Correlation Energies in Water Clusters. ChemPhysChem. 18(23). 3360–3368. 16 indexed citations

10.

McDonagh, James L., et al.. (2016). Bringing computational science to the public. SpringerPlus. 5(1). 259–259. 3 indexed citations

11.

McDonagh, James L., et al.. (2016). Implementation of Recovery Programming on an Inpatient Acute Psychiatric Unit and Its Impact on Readmission. Journal of Addictions Nursing. 27(2). 101–108. 9 indexed citations

12.

McDonagh, James L., Mark A. Vincent, & Paul L. A. Popelier. (2016). Partitioning dynamic electron correlation energy: Viewing Møller-Plesset correlation energies through Interacting Quantum Atom (IQA) energy partitioning. Chemical Physics Letters. 662. 228–234. 32 indexed citations

13.

McDonagh, James L., David Scott Palmer, Tanja van Mourik, & John B. O. Mitchell. (2016). Are the Sublimation Thermodynamics of Organic Molecules Predictable?. Journal of Chemical Information and Modeling. 56(11). 2162–2179. 29 indexed citations

14.

Barker, Daniel, et al.. (2015). University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school. International Journal of STEM Education. 2(1). 5 indexed citations

15.

McDonagh, James L., et al.. (2015). Predicting Melting Points of Organic Molecules: Applications to Aqueous Solubility Prediction Using the General Solubility Equation. Molecular Informatics. 34(11-12). 715–724. 28 indexed citations

16.

Skyner, R., James L. McDonagh, Colin R. Groom, Tanja van Mourik, & John B. O. Mitchell. (2015). A review of methods for the calculation of solution free energies and the modelling of systems in solution. Physical Chemistry Chemical Physics. 17(9). 6174–6191. 406 indexed citations breakdown →

17.

McDonagh, James L., et al.. (2014). Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules. Journal of Chemical Information and Modeling. 54(3). 844–856. 73 indexed citations

18.

Mavridis, Lazaros, et al.. (2012). Enzyme Informatics. Current Topics in Medicinal Chemistry. 12(17). 1911–1923. 16 indexed citations

19.

Palmer, David Scott, James L. McDonagh, John B. O. Mitchell, Tanja van Mourik, & Maxim V. Fedorov. (2012). First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules. Journal of Chemical Theory and Computation. 8(9). 3322–3337. 82 indexed citations

20.

McDonagh, James L., et al.. (2007). Efficient construction and implementation of short LDPC codes for wireless sensor networks. Institutional Research Information System (Università degli Studi di Trento). 703–706. 5 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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