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Deep learning for molecular design—a review of the state of the art
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.Expand
Applying machine learning techniques to predict the properties of energetic materials
TLDR
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. Expand
Machine Learning of Energetic Material Properties
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, analysisExpand
Deep learning for molecular generation and optimization - a review of the state of the art
TLDR
Several important high level themes emerge, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training. Expand
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
TLDR
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. Expand
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
TLDR
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. Expand
Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials
TLDR
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. Expand
Locally Optimizable Joint Embedding Framework to Design Nitrogen‐rich Molecules that are Similar but Improved
TLDR
A deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class, demonstrating that fusing and joint embedding with plentiful low nitrogen molecular data can produce higher generative performance than using the scarce data alone. Expand