Brian C. Barnes

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Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials

Brian C. Barnes, Kenneth W. Leiter, James P. Larentzos, 2020 .

Computer system with associated multimedia processor and multimedia storage

Brian C. Barnes, Dale E. Gulick, Andy Lambrecht, 1996 .

Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials

Klavs F. Jensen, Brian C. Barnes, Connor W. Coley, 2020, SHOCK COMPRESSION OF CONDENSED MATTER - 2019: Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter.

Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials

Betsy M. Rice, Brian C. Barnes, James P. Larentzos, 2019, Computational Approaches for Chemistry Under Extreme Conditions.

Accelerated scale-bridging through adaptive surrogate model evaluation

Jaroslaw Knap, Brian C. Barnes, Kenneth W. Leiter, 2018, J. Comput. Sci..

Computer system having an expansion bus and an associated real-time bus for increasing the multimedia performance

Brian C. Barnes, Dale E. Gulick, Andy Lambrecht, 1996 .

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

Jaroslaw Knap, Brian C. Barnes, Kenneth W. Leiter, 2016 .

Windows 95 Win 32 Programming Api Bible

Richard J. Simon, Brian C. Barnes, Michael Gouker, 1996 .

Processor with input / Ausgabeerlaubnisbitstrukturen for divided in areas of security and method for selectively performing an input / output instruction

Brian C. Barnes, Rodney W. Drippings Springs Schmidt, Geoffrey S. Austin Strongin, 2002 .

Machine Learning of Energetic Material Properties and Performance

Betsy M. Rice, Brian C. Barnes, Andrew Sifain, 2020 .

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

Jaroslaw Knap, Brian C. Barnes, Kenneth W. Leiter, 2015 .

multicadenas connected messages microprocessor configured to execute service routines switch as a chain of connected messages.

David S. Christie, Brian C. Barnes, Drew J. Dutton, 1997 .

Memory management unit, method for providing a memory access security on the basis of a linear address, and processor

Brian C. Barnes, Rodney Schmidt, 2002 .

System and method for treating device accesses to a memory with improved memory access security

Brian C. Barnes, Geoffrey S. Austin Strongin, Rodney Schmidt, 2002 .

Windows 95 WIN32 Programming API Bible with Cdrom

Richard J. Simon, Brian C. Barnes, Michael Gouker, 1995 .

System and method for controlling the access between devices within a computer system

Rodney Dripping Springs Schmidt, Brian C. Barnes, Geoffrey S. Austin Strongin, 2002 .

Deep learning for energetic material detonation performance

Brian C. Barnes, 2020 .

Machine Learning of Energetic Material Properties

William D. Mattson, Zois Boukouvalas, Brian C. Barnes, 2018, 1807.06156.

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

Brian C. Barnes, 2018 .

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

Brian C. Barnes, 2010 .

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

Jaroslaw Knap, Brian C. Barnes, Kenneth W. Leiter, 2017 .

Prediction of thermal decomposition temperatures using statistical methods

Brian C. Barnes, A. Beste, 2019 .

Machine learning transition temperatures from 2D structure.

Brian C. Barnes, S. Yalkowsky, B. Rice, 2019, Journal of molecular graphics & modelling.

Seeking Ways to Break Energy Storage Limits

Brian C. Barnes, W. Mattson, B. Rice, 2018 .

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

Brian C. Barnes, Connor W. Coley, Michael E. Fortunato, 2020, J. Chem. Inf. Model..

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

Brian C. Barnes, K. Jensen, B. Rice, 2022, J. Chem. Inf. Model..

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

Brian C. Barnes, James P. Larentzos, J. Brennan, 2022, Journal of Computational Physics.

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

Brian C. Barnes, Sergei Izvekov, Martin Lísal, 2016, The Journal of chemical physics.

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

Ilias Bilionis, Brian C. Barnes, Alex D Casey, 2020, J. Chem. Inf. Model..

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

Brian C. Barnes, T. Vaid, and Abigail K. R. Lytton-Jean, 2003 .

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

Brian C. Barnes, Connor W. Coley, K. Jensen, 2020, SHOCK COMPRESSION OF CONDENSED MATTER - 2019: Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter.

Structure, thermodynamics, and solubility in tetromino fluids.

Brian C. Barnes, Brian C Barnes, Lev D Gelb, 2009, Langmuir : the ACS journal of surfaces and colloids.

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

Brian C. Barnes, Brian C Barnes, Lev D Gelb, 2007, Journal of chemical theory and computation.

Nucleation rate analysis of methane hydrate from molecular dynamics simulations.

Brian C. Barnes, A. Sum, G. Beckham, 2015, Faraday discussions.

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

Brian C. Barnes, Brian C Barnes, Gregg T Beckham, 2014, The Journal of chemical physics.

Advances in molecular simulations of clathrate hydrates

Brian C. Barnes, A. Sum, 2013 .

Machine learning for shock compression of solids using scarce data

Brian C. Barnes, Peter W. Chung, M. Fuge, 2023, Journal of Applied Physics.

Reaction coordinate of incipient methane clathrate hydrate nucleation.

Brian C. Barnes, A. Sum, G. Beckham, 2014, The journal of physical chemistry. B.

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

Brian C. Barnes, Peter W. Chung, M. Fuge, 2021, Molecular informatics.

Observation of interstitial molecular hydrogen in clathrate hydrates.

Brian C. Barnes, A. Soper, A. Sum, 2014, Angewandte Chemie.

Molecular dynamics simulations of the formation of ethane clathrate hydrates

Brian C. Barnes, A. Sum, David T Wu, 2016 .

Voronoi Tessellation Analysis of Clathrate Hydrates

Brian C. Barnes, A. Sum, David T Wu, 2012 .

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

Ilias Bilionis, Steven F. Son, Brian C. Barnes, 2020 .