Brian C. Barnes

Most-cited papers

1. Reaction coordinate of incipient methane clathrate hydrate nucleation.

   Brian C. Barnes, B. Knott, G. Beckham, David T. Wu +1 more

   2014Journal of Physical Chemistry B97 citations

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2. Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks

   Alex D. Casey, S. Son, Ilias Bilionis, Brian C. Barnes

   2020Journal of Chemical Information and Modeling82 citations

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3. Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

   Michael E. Fortunato, Connor W. Coley, Brian C. Barnes, K. Jensen

   2020Journal of Chemical Information and Modeling78 citations Open access

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4. Advances in molecular simulations of clathrate hydrates

   Brian C. Barnes, A. Sum

   201373 citations

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5. Two-component order parameter for quantifying clathrate hydrate nucleation and growth.

   Brian C. Barnes, G. Beckham, David T. Wu, A. Sum

   2014Journal of Chemical Physics67 citations

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6. A coarse-grain force field for RDX: Density dependent and energy conserving.

   Joshua D. Moore, Brian C. Barnes, S. Izvekov, M. Lísal +3 more

   2016Journal of Chemical Physics66 citations Open access

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7. Nucleation rate analysis of methane hydrate from molecular dynamics simulations.

   Daisuke Yuhara, Brian C. Barnes, D. Suh, B. Knott +4 more

   2015Faraday discussions57 citations Open access

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8. Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach

   Joshua L. Lansford, Brian C. Barnes, B. Rice, K. Jensen

   2022Journal of Chemical Information and Modeling35 citations

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9. Accelerated scale-bridging through adaptive surrogate model evaluation

   K. Leiter, Brian C. Barnes, R. Becker, J. Knap

   2018Journal of Computer Science35 citations Open access

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10. Structure, thermodynamics, and solubility in tetromino fluids.

    Brian C. Barnes, D. Siderius, L. Gelb

    2009Langmuir30 citations Open access

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11. Voronoi Tessellation Analysis of Clathrate Hydrates

    S. N. Chakraborty, Eric M. Grzelak, Brian C. Barnes, David T. Wu +1 more

    201224 citations

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12. Machine Learning of Energetic Material Properties

    Brian C. Barnes, Daniel C. Elton, Z. Boukouvalas, Decarlos E. Taylor +3 more

    201822 citations

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13. Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials

    Brian C. Barnes, K. Leiter, J. Larentzos, J. Brennan

    202020 citations

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14. Molecular dynamics simulations of the formation of ethane clathrate hydrates

    Daniel T. Wilson, Brian C. Barnes, David T. Wu, A. Sum

    201620 citations Open access

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15. Deep learning for energetic material detonation performance

    Brian C. Barnes

    202018 citations

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16. Observation of interstitial molecular hydrogen in clathrate hydrates.

    R. Grim, Brian C. Barnes, Patrick G. Lafond, Winfred A. Kockelmann +6 more

    2014Angewandte Chemie17 citations

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17. LAMMPS integrated materials engine (LIME) for efficient automation of particle-based simulations: application to equation of state generation

    Brian C. Barnes, K. Leiter, R. Becker, J. Knap +1 more

    201716 citations Open access

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18. Investigations of the 9,10-Diphenylacridyl Radical as an Isostructural Dopant for the Molecular Semiconductor 9,10-Diphenylanthracene

    T. Vaid, Abigail K. Lytton-Jean, Brian C. Barnes

    200316 citations

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19. Locally Optimizable Joint Embedding Framework to Design Nitrogen‐rich Molecules that are Similar but Improved

    Sangeeth Balakrishnan, Francis G. VanGessel, Z. Boukouvalas, Brian C. Barnes +2 more

    2021Molecular Informatics12 citations

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20. Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials

    Brian C. Barnes, J. Brennan, E. Byrd, S. Izvekov +2 more

    2019Computational Approaches for Chemistry Under Extreme Conditions12 citations

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21. Prediction of thermal decomposition temperatures using statistical methods

    Ariana Beste, Brian C. Barnes

    201911 citations

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22. Hierarchical multiscale framework for materials modeling: Equation of state implementation and application to a Taylor anvil impact test of RDX

    Brian C. Barnes, C. Spear, K. Leiter, R. Becker +3 more

    201510 citations Open access

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23. Temporal scale-bridging of chemistry in a multiscale model: Application to reactivity of an energetic material

    K. Leiter, J. Larentzos, Brian C. Barnes, J. Brennan +2 more

    2022Journal of Computational Physics9 citations

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24. Meta-Optimization of Evolutionary Strategies for Empirical Potential Development: Application to Aqueous Silicate Systems.

    Brian C. Barnes, L. Gelb

    2007Journal of Chemical Theory and Computation8 citations

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25. Machine learning transition temperatures from 2D structure.

    Andrew E. Sifain, B. Rice, S. Yalkowsky, Brian C. Barnes

    2019Journal of Molecular Graphics and Modelling6 citations Open access

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26. Machine Learned Prediction of Reaction Template Applicability for Data-Driven Retrosynthetic Predictions of Energetic Materials

    Michael E. Fortunato, Connor W. Coley, Brian C. Barnes, K. Jensen

    2020SHOCK COMPRESSION OF CONDENSED MATTER - 2019: Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter5 citations Open access

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27. Development of Atomistic Potentials for Silicate Materials and Coarse-Grained Simulation of Self-Assembly at Surfaces

    Brian C. Barnes

    20103 citations

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28. Machine learning for shock compression of solids using scarce data

    Sangeeth Balakrishnan, Francis G. VanGessel, Brian C. Barnes, Ruth M. Doherty +4 more

    2023Journal of Applied Physics2 citations Open access

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29. Machine Learning of Energetic Material Properties and Performance

    Brian C. Barnes, B. Rice, Andrew E. Sifain

    20202 citations

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30. Machine Learning for Thermodynamic States of Notional Nonideal Energetic Mixtures

    Sangeeth Balakrishnan, Jesse C. Hearn, Francis G. VanGessel, Brian C. Barnes +4 more

    2026Propellants, explosives, pyrotechnics0 citations Open access

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31. Understanding the Role of Microstructure in Energetic Materials Using a Predictive Hierarchical Multiscale Simulation Approach

    J. Larentzos, Brian C. Barnes, K. Leiter, J. Brennan +3 more

    20200 citations

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32. Deep Learning for Energetic Materials: Predicting Material Properties from Electronic Structure using Convolutional Neural Networks

    Alex D. Casey, Brian C. Barnes, Ilias Bilionis, S. Son

    20200 citations

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33. Data-Driven Retrosynthetic Predictions for Energetic Materials

    Michael E. Fortunato, Connor W. Coley, Brian C. Barnes, I. Schweigert +2 more

    20190 citations

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34. Machine Learning for Energetic Material Detonation Performance

    Brian C. Barnes

    20190 citations

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35. A Primer on Machine Learning for Materials and Its Relevance to Army Challenges

    Brian C. Barnes

    20180 citations

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36. Seeking Ways to Break Energy Storage Limits

    William D. Mattson, Brian C. Barnes, B. Rice

    20180 citations

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37. Compression and Decompression of RDX [100] via Large-scale Simulation

    Brian C. Barnes, S. Izvekov, N. Weingarten

    20170 citations

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38. Hierarchical Multiscale Simulation: Scale-Bridging for Shock Response of Energetic Materials

    Brian C. Barnes

    20170 citations

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39. Hierarchical Multiscale Framework for Materials Modeling: Advances in Scale-Bridging Applied to a Taylor Anvil Impact Test of RDX

    Brian C. Barnes, K. Leiter, R. Becker, J. Knap +1 more

    20160 citations

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40. Atomistic charge-transfer potentials for silica and aqueous silicates

    Brian C. Barnes, L. Gelb

    20080 citations

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41. AIChE Annual Meeting, Conference Proceedings

    Brian C. Barnes, D. Siderius, L. Gelb

    20080 citations

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42. Monte Carlo simulations of tetrominoes

    Brian C. Barnes, D. Siderius, L. Gelb

    20080 citations

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43. Monte Carlo simulations of tetrominoes: Structural and thermodynamic properties of a seven-component on-lattice fluid model

    Brian C. Barnes, D. Siderius, L. Gelb

    20080 citations

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