Machine Learning Tools and their Applications to Novel Energetics

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

Webinar Series

Promoting the transfer of innovative, cost-effective and sustainable solutions.

View Webinar Schedule


Posts highlighting research, technologies, and tools.

Browse Blog


Schedule of events, solicitation deadlines, and training opportunities.

View Calendar

Headlines & Updates Promo

SERDP and ESTCP Newsletters


SERDP-ESTCP Headlines 2012 - Summer
Winter 2021

   Past Headlines

Program Area Updates

SERDP-ESTCP Headlines 2012 - Summer
Energy and Water
November 2019

Past Program Area Updates