Brian C. Barnes

953 total citations
33 papers, 675 citations indexed

About

Brian C. Barnes is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Environmental Chemistry. According to data from OpenAlex, Brian C. Barnes has authored 33 papers receiving a total of 675 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Materials Chemistry, 10 papers in Computational Theory and Mathematics and 8 papers in Environmental Chemistry. Recurrent topics in Brian C. Barnes's work include Machine Learning in Materials Science (10 papers), Computational Drug Discovery Methods (10 papers) and Spacecraft and Cryogenic Technologies (8 papers). Brian C. Barnes is often cited by papers focused on Machine Learning in Materials Science (10 papers), Computational Drug Discovery Methods (10 papers) and Spacecraft and Cryogenic Technologies (8 papers). Brian C. Barnes collaborates with scholars based in United States, Japan and Czechia. Brian C. Barnes's co-authors include Amadeu K. Sum, David T. Wu, Gregg T. Beckham, Brandon C. Knott, Klavs F. Jensen, Steven F. Son, John K. Brennan, Ilias Bilionis, Lev D. Gelb and Kenneth Leiter and has published in prestigious journals such as Angewandte Chemie International Edition, The Journal of Chemical Physics and Chemistry of Materials.

In The Last Decade

Brian C. Barnes

31 papers receiving 662 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian C. Barnes United States	14	278	263	184	173	100	33	675
K. S. Glavatskiy Australia	15	274 1.0×	88 0.3×	140 0.8×	97 0.6×	5 0.1×	29	601
Philip C. Myint United States	13	52 0.2×	165 0.6×	301 1.6×	22 0.1×	27 0.3×	30	744
José Manuel Mı́guez Spain	18	373 1.3×	173 0.7×	189 1.0×	106 0.6×	3 0.0×	39	937
Bernard A. Baldwin United States	19	322 1.2×	154 0.6×	619 3.4×	39 0.2×	5 0.1×	51	1.2k
A. Barreau France	19	37 0.1×	168 0.6×	72 0.4×	26 0.2×	12 0.1×	50	769
F. D. A. Aarão Reis Brazil	21	48 0.2×	467 1.8×	87 0.5×	6 0.0×	70 0.7×	116	1.5k
Patsy S. Chappelear United States	17	141 0.5×	165 0.6×	112 0.6×	90 0.5×	3 0.0×	27	1.3k
H. D. Australia	13	27 0.1×	241 0.9×	208 1.1×	14 0.1×	6 0.1×	20	826
V. V. Ivanov Russia	15	21 0.1×	179 0.7×	53 0.3×	41 0.2×	15 0.1×	151	893
С. С. Сафонов Russia	15	64 0.2×	82 0.3×	164 0.9×	9 0.1×	9 0.1×	50	709

Countries citing papers authored by Brian C. Barnes

Since Specialization

Citations

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

Fields of papers citing papers by Brian C. Barnes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian C. Barnes. 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 Brian C. Barnes. The network helps show where Brian C. Barnes may publish in the future.

Co-authorship network of co-authors of Brian C. Barnes

This figure shows the co-authorship network connecting the top 25 collaborators of Brian C. Barnes. A scholar is included among the top collaborators of Brian C. Barnes 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 Brian C. Barnes. Brian C. Barnes 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

Barnes, Brian C., et al.. (2025). Data Fusion of Deep Learned Molecular Embeddings for Property Prediction. Journal of Chemical Information and Modeling. 65(21). 11620–11630. 1 indexed citations

Taylor, Michael Alan, et al.. (2024). Multi‐Task Multi‐Fidelity Learning of Properties for Energetic Materials. Propellants Explosives Pyrotechnics. 50(1). 4 indexed citations

Rice, Betsy M., et al.. (2024). Predicting Hydrocarbon Strain Energy via a Group Equivalent Machine Learning Approach. The Journal of Physical Chemistry A. 128(35). 7489–7497.

Barnes, Brian C., et al.. (2022). Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach. Journal of Chemical Information and Modeling. 62(22). 5397–5410. 23 indexed citations

Leiter, Kenneth, James P. Larentzos, Brian C. Barnes, et al.. (2022). Temporal scale-bridging of chemistry in a multiscale model: Application to reactivity of an energetic material. Journal of Computational Physics. 472. 111682–111682. 1 indexed citations

Sifain, Andrew E., Betsy M. Rice, Samuel H. Yalkowsky, & Brian C. Barnes. (2021). Machine learning transition temperatures from 2D structure. Journal of Molecular Graphics and Modelling. 105. 107848–107848. 4 indexed citations

Balakrishnan, Sangeeth, et al.. (2021). Locally Optimizable Joint Embedding Framework to Design Nitrogen‐rich Molecules that are Similar but Improved. Molecular Informatics. 40(7). e2100011–e2100011. 9 indexed citations

Coley, Connor W., et al.. (2020). Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning. Journal of Chemical Information and Modeling. 60(7). 3398–3407. 48 indexed citations

Barnes, Brian C., Betsy M. Rice, & Andrew E. Sifain. (2020). Machine Learning of Energetic Material Properties and Performance. Bulletin of the American Physical Society. 3 indexed citations

10.

Son, Steven F., et al.. (2020). Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks. Journal of Chemical Information and Modeling. 60(10). 4457–4473. 59 indexed citations

11.

Barnes, Brian C.. (2020). Deep learning for energetic material detonation performance. AIP conference proceedings. 3294. 70002–70002. 11 indexed citations

12.

Coley, Connor W., et al.. (2020). Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials. AIP conference proceedings. 2272. 70014–70014. 4 indexed citations

13.

Leiter, Kenneth, Brian C. Barnes, Richard Becker, & Jaroslaw Knap. (2018). Accelerated scale-bridging through adaptive surrogate model evaluation. Journal of Computational Science. 27. 91–106. 25 indexed citations

14.

Barnes, Brian C., Donguk Suh, Brandon C. Knott, et al.. (2015). Nucleation rate analysis of methane hydrate from molecular dynamics simulations. Faraday Discussions. 179. 463–474. 53 indexed citations

15.

Wilson, Daniel T., Brian C. Barnes, David T. Wu, & Amadeu K. Sum. (2015). Molecular dynamics simulations of the formation of ethane clathrate hydrates. Fluid Phase Equilibria. 413. 229–234. 17 indexed citations

16.

Grim, R. Gary, Brian C. Barnes, Patrick G. Lafond, et al.. (2014). Observation of Interstitial Molecular Hydrogen in Clathrate Hydrates. Angewandte Chemie International Edition. 53(40). 10710–10713. 10 indexed citations

17.

Barnes, Brian C., Gregg T. Beckham, David T. Wu, & Amadeu K. Sum. (2014). Two-component order parameter for quantifying clathrate hydrate nucleation and growth. The Journal of Chemical Physics. 140(16). 164506–164506. 58 indexed citations

18.

Barnes, Brian C., Daniel W. Siderius, & Lev D. Gelb. (2009). Structure, Thermodynamics, and Solubility in Tetromino Fluids. Langmuir. 25(12). 6702–6716. 32 indexed citations

19.

Barnes, Brian C. & Lev D. Gelb. (2007). Meta-Optimization of Evolutionary Strategies for Empirical Potential Development: Application to Aqueous Silicate Systems. Journal of Chemical Theory and Computation. 3(5). 1749–1764. 6 indexed citations

20.

Rinaldi, David, et al.. (2001). A Phase I–II Trial of Topotecan and Gemcitabine in Patients with Previously Treated, Advanced Non-small Cell Lung Cancer (LOA-3). Cancer Investigation. 19(5). 467–474. 9 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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