Optimization of materials has been the basis of considerable material discovery in recent years due to the capability to traverse and search large chemical spaces within feasible computational time. In addition, the ability to constrain the optimization procedure has made this process invaluable towards the development of materials from drug design to catalyst discovery. Common first-principles optimizations have mostly restricted themselves to single constraints. We have developed a multiple-constraint optimization procedure and have tested on the search for practical candidates for novel energetic materials in the present work. The nature of the energetic materials strongly lends itself to multiple-constraint optimization as there are several key factors that must be controlled during optimization. To perform the optimization procedure, an optimization framework was chosen for generation of the chemical subspace and within this subspace there are 10<sup>10</sup> possible candidates. A primal-dual algorithm of first order with multiple Lagrange multipliers was used to facilitate the search. Calculations have been performed using density functional theory and then input into the optimization procedure. Each of the key properties including synthetic accessibility, detonation pressure, shock velocity and total energy of detonation are analyzed for their contribution to finding and generating viable energetic candidates. It was found that the method to search through this vast chemical subspace is highly efficient and is able to find energetic candidates that are accessible for practical application.   <br/>

Exploring novel energetic materials: A constrained search approach

249th National Meeting (2015)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.