Machine Learning Aids in Materials Design. A long-held goal by chemists across many industries, including energy, pharmaceuticals, energetics, food additives and organic semiconductors, is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify and characterize newly designed molecules to obtain the desired information. Recently, a team of Lawrence Livermore National Laboratory (LLNL) materials and computer scientists have brought this vision to fruition for energetic molecules by ...