Machine Learning to Accelerate Design of Energetic Materials

An apparatus for predictive analytics, an apparatus that employs a prediction module, for the purpose of efficiently searching composition space of energetic materials of interest, with energetic materials comprising of propellants, explosives or pyrotechnics, and hence accelerating identification of energetic materials with desired characteristics, an apparatus comprising of a database importing module, for ingesting relevant materials data,

• an optional data base abstraction module, also referred to as prediction logic, for abstracting an interface between a prediction module and the database,
• a prediction module, also referred to as optimization module, prediction engine or optimization engine, for predicting or optimizing material properties of interest given the materials data ingested, where the prediction module applies a prediction or optimization technique, suitable for the data available and the problem at hand, to a set of descriptors characteristic of energetic materials, where the prediction module is capable of accounting for physics-based models for improved prediction or optimization accuracy, and where the prediction or optimization technique is selected such that the number of unknown parameters in a model employed by the prediction or optimization technique does not exceed the number of data points available for estimating or training the model, and
• one or more optional interfaces with, or being integrated into physics-based modeling tools providing calculations of thermo-physical or thermo-chemical properties, first-principle (ab initio) calculations, molecular dynamics simulations or group additivity methods,
• an optional reporting and verification module, for reporting and verifying the materials properties estimated,
• an apparatus presented either in the form of an integrated or an embedded application.