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1. Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials

Author

Appleton, Robert J., Klinger, Daniel, Lee, Brian H., Taylor, Michael, Kim, Sohee, Blankenship, Samuel, Barnes, Brian C., Son, Steven F., and Strachan, Alejandro

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science
Abstract

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials., Comment: 16 pages, 4 figures, 2 tables

Published
2024

2. Machine Learning of Energetic Material Properties

Author

Barnes, Brian C., Elton, Daniel C., Boukouvalas, Zois, Taylor, DeCarlos E., Mattson, William D., Fuge, Mark D., and Chung, Peter W.

Subjects
Condensed Matter - Materials Science, Physics - Chemical Physics, Physics - Computational Physics
Abstract

In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysis is applied to individual molecules in order to explore the contribution of bonding motifs to these properties. Feature descriptors evaluated include Morgan fingerprints, E-state vectors, a custom "sum over bonds" descriptor, and coulomb matrices. Algorithms discussed include kernel ridge regression, least absolute shrinkage and selection operator ("LASSO") regression, Gaussian process regression, and the multi-layer perceptron (a neural network). Effects of regularization, kernel selection, network parameters, and dimensionality reduction are discussed. We determine that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials., Comment: Preprint for publication in the Proceedings of the 16th International Detonation Symposium

Published
2018

4. Predicting Hydrocarbon Strain Energy via a Group Equivalent Machine Learning Approach

Author

Hearn, Jesse C., Rice, Betsy M., Barnes, Brian C., and Chung, Peter W.

Abstract

Strain energy is a fundamental measure of the steric and configurational properties of organic molecules. The ability to estimate strain energy through quantum chemical simulations requires at minimum the knowledge of an initial set of nuclear coordinates. In general, such knowledge is not categorically known when screening or generating large numbers of molecule candidates in the context of molecular design. We present a machine learning approach to predict hydrocarbon strain energies using Benson group equivalents. A featurization strategy is crafted by concatenating the molecule group equivalent counts with easily computable molecular fingerprints. The data are obtained from electronic structure calculations we performed on a set of 166 previously synthesized strained hydrocarbons. These data are provided and include gas phase enthalpies of formation and associated optimized atomic coordinates. The strain energy prediction accuracy of several statistical learning methods is evaluated, and their respective merits and limitations are discussed.

Published
2024
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7. Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

Author

Massachusetts Institute of Technology. Department of Chemical Engineering, Fortunato, Michael E, Coley, Connor W, Barnes, Brian C, Jensen, Klavs F, Massachusetts Institute of Technology. Department of Chemical Engineering, Fortunato, Michael E, Coley, Connor W, Barnes, Brian C, and Jensen, Klavs F

Abstract

Copyright © 2020 American Chemical Society. This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high-accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access data sets of organic reactions with explicitly calculated template applicability and pretraining a template-relevance neural network on this augmented applicability data set, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small data set of well-curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating that these strategies can be very useful for small data sets.

Published
2021

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

Author

Massachusetts Institute of Technology. Department of Chemical Engineering, Fortunato, Michael E, Coley, Connor Wilson, Barnes, Brian C, Jensen, Klavs F, Massachusetts Institute of Technology. Department of Chemical Engineering, Fortunato, Michael E, Coley, Connor Wilson, Barnes, Brian C, and Jensen, Klavs F

Abstract

State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction template applicability for small organic molecules to supplement the rare reaction examples of relevance to energetic materials. The training data for the neural network was generated by brute force determination of template subgraph matching for product molecules from a database of reactions in U.S. patent literature. This data generation strategy successfully augmented the information about template applicability for rare reaction mechanisms in the reaction database. The increased ability to recognize rare reaction templates was demonstrated for reaction templates of interest for energetic material synthesis such as heterocycle ring formation., U.S. Army Research Laboratory (Award W911NF-19-2-0034)

Published
2021

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

Author

Barnes, Brian C., Beckham, Gregg T., Wu, David T., and Sum, Amadeu K.

Subjects
*GAS hydrates, *NUCLEATION, *MOLECULAR dynamics, *MOLECULAR clusters, *WATER clusters
Abstract

Methane clathrate hydrate nucleation and growth is investigated via analysis of molecular dynamics simulations using a new order parameter. This order parameter (OP), named the Mutually Coordinated Guest (MCG) OP, quantifies the appearance and connectivity of molecular clusters composed of guests separated by water clusters. It is the first two-component OP used for quantifying hydrate nucleation and growth. The algorithm for calculating the MCG OP is described in detail. Its physical motivation and advantages compared to existing methods are discussed. [ABSTRACT FROM AUTHOR]

Published
2014
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11. ReactionCoordinate of Incipient Methane ClathrateHydrate Nucleation.

Author

Barnes, Brian C., Knott, Brandon C., Beckham, Gregg T., Wu, David T., and Sum, Amadeu K.

Subjects
*CHEMICAL reactions, *METHANE hydrates, *HYDRATES, *NUCLEATION, *SOLUTION (Chemistry), *BIOMINERALIZATION
Abstract

Nucleation from solution is a ubiquitousphenomenon with relevanceto myriad scientific disciplines, including pharmaceuticals, biomineralization,and disease. One prominent example is the nucleation of clathratehydrates, multicomponent crystalline inclusion compounds relevantto the energy industry where they block pipelines and also constitutea potential vast energy resource. Despite their importance, the molecularmechanism of incipient hydrate formation remains unknown. Herein,we employ advanced molecular simulation tools (pBhistogram, equilibrium path sampling) to provide a statistical-mechanicalbasis for extracting physical insight into the molecular steps bywhich clathrates form. Through testing the Mutually Coordinated Guest(MCG) order parameter, we demonstrate that both guest (methane) andhost (water) structuring are crucial to accurately describe the nucleationof hydrates and determine a critical nucleus size of MCG-1 = 16 at255 K and 500 bar. Equipped with a validated (and novel) reactioncoordinate, subsequent equilibrium path sampling simulations yieldthe free energy barrier and nucleation rate. The resulting quantitativenucleation process is described by the MCG clustering mechanism. Thisconstitutes a significant advance in the field of hydrates research,as the fitness of a molecular descriptor has never been statisticallyverified. More broadly, this work has significance to a wide rangeof multicomponent nucleation contexts wherein the formation mechanismdepends on contributions from both solute and solvent. [ABSTRACT FROM AUTHOR]

Published
2014
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12. Legal Research on the Internet.

Author

Barnes, Brian C.

Subjects
*WEBSITES, *LEGAL research, *REFERENCE librarians, *GOVERNMENT websites, *EDUCATIONAL websites
Abstract

The article discusses some of the Web sites on legal research available for a reference librarian to use and how to best use such resources for library patrons. State government Web pages for Mississippi include the state Web site at www.mississippi.gov, the legislature's Web site at billstatus.ls.state.ms.us, and the judiciary site at www.mssc.state.ms.us. Examples of educational Web sites include the Cornell Legal Information Institute, the University of Richmond Constitution Funder and the Mississippi College School of Law Mississippi Resources page.

Published
2008

13. Instruction solves CPU's identity crisis

Author

Teich, Paul and Barnes, Brian C.

Subjects
Technology overview, Microprocessor, Microprocessors -- Design and construction, Semiconductor industry -- Design and construction
Abstract

The proliferation of multimedia-enabled applications and X86-compatible processors and feature sets demands a quick and easy means for software developers to determine the features and performance level of a given […]

Published
1997

14. Localization of Free Fatty Acids Taken Up by Human Platelets

Author

Hoak, John C., Spector, Arthur A., Fry, Glenna L., and Barnes, Brian C.

Abstract

Human platelets take up and metabolize long-chain free fatty acids (FFA). Experiments in which platelets were loaded with 14C-palmitate, homogenized, and then fractionated indicated large amounts of membrane-bound FFA. In other studies, platelet membrane fractions took up FFA from incubation media containing albumin-bound FFA. These findings suggested that a large portion of the platelet FFA uptake might be associated with the platelet surface. Radioautography with electron microscopy using 3H-palmitate-labeled platelets provided additional evidence that much of the newly incorporated FFA was associated with the platelet surface.

Published
1972
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