Advances in synthesis methodology can have an impact on the selection of starting materials, synthesis process steps, and isolation of energetic materials. Current industrial processes for the production of energetic materials do not take into account hazardous materials and wastes. The application of modern and novel computational tools can reduce the environmental footprint of these large scale industrial processes. This short course covered recent research and development of machine learning techniques for application to energetic materials. It will cover two distinctly different topics for model development: property prediction and synthetic chemistry planning. The course reviewed the state-of-the-art, described some recent DoD results, and provided practical advice for getting started in the field. Data collection and curation, data representation, classes of algorithms (e.g., neural network models), and model evaluation methods were also discussed.

Session Chair: Dr. Nirupam Trivedi, U.S. Army Research Laboratory

Introduction by Session Chair                                                           

Dr. Nirupam Trivedi, U.S. Army Research Laboratory

Machine Learning for Molecules

 Dr. Brian Barnes, U.S. Army Research Laboratory