# Machine Learning of Energetic Material Properties

@article{Barnes2018MachineLO, title={Machine Learning of Energetic Material Properties}, author={Brian C. Barnes and Daniel C. Elton and Zois Boukouvalas and DeCarlos E. Taylor and William D. Mattson and Mark D. Fuge and Peter W. Chung}, journal={arXiv: Materials Science}, year={2018} }

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…

## 11 Citations

Applying machine learning techniques to predict the properties of energetic materials

- Computer Science, ChemistryScientific Reports
- 2018

This work presents a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints - and concludes that the best featurizing was sum over bond counting, and the best model was kernel ridge regression.

Applying machine learning to balance performance and stability of high energy density materials

- Computer ScienceiScience
- 2021

Machine learning transition temperatures from 2D structure.

- ChemistryJournal of molecular graphics & modelling
- 2021

This work extends a molecular representation, or set of descriptors, first developed for quantitative structure-property relationship modeling by Yalkowsky and coworkers known as the Unified Physicochemical Property Estimation Relationships (UPPER), and uses the UPPER descriptors to construct a vector representation for use with machine learning techniques.

Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning

- Computer ScienceArXiv
- 2018

This work proposes a data fusion framework that uses Independent Vector Analysis to exploit underlying complementary information contained in different molecular featurization methods, bringing us a step closer to automated feature generation.

Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials

- Computer Science2020 28th European Signal Processing Conference (EUSIPCO)
- 2021

This work proposes a data fusion framework that uses Independent Vector Analysis to uncover underlying complementary information contained in different molecular featurization methods and generates a low dimensional set of features—molecular signatures—that can be used for the prediction of molecular properties and for knowledge discovery.

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

- Materials SciencePolymers
- 2020

This study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications.

Generating valid Euclidean distance matrices

- Computer ScienceArXiv
- 2019

This architecture is used to construct a Wasserstein GAN utilizing a permutation invariant critic network, which makes it possible to generate molecular structures in a one-shot fashion by producing Euclidean distance matrices which have a three-dimensional embedding.

Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

- Computer ScienceArXiv
- 2019

This work explores how techniques from natural language processing and machine learning can be used to automatically extract chemical insights from large collections of documents and compares the utility of two popular word embeddings.

Common Pitfalls When Explaining AI and Why Mechanistic Explanation Is a Hard Problem

- Computer ScienceICICT
- 2021

It is suggested that mechanistic explanation of deep neural networks will be very challenging for most real-world applications and more focus should be given to implementing out-of-distribution detection methods to detect when a model is extrapolating and thus is likely to fail.

Self-explaining AI as an Alternative to Interpretable AI

- Computer ScienceAGI
- 2020

It is argued it is important that deep learning based systems include a "warning light" based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution.

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