A Deep Learning Approach to Searching Property Spaces of Materials

Abstract

Melt processing of molecular crystals has several advantages over alternative routes for manufacturing materials such as pharmaceuticals, organic photovoltaics, and energetic materials. The experimental characterization of the materials properties required to assess melt processability (melting temperature, boiling temperature, decomposition temperature and vapor pressure) for the 1.3 million known molecular crystals is unfeasible; in fact, our survey of the research literature and open databases resulted in only 43 molecular materials with experimentally measured properties that satisfy a common criterion for melt-casting. We developed multi-task, graph-based neural network models that simultaneously predict these properties using a molecular graph as the only input. Screening databases of known molecules with our ML model resulted 2,532 melt-castable candidates, with melting temperature between 343K and 393K, boiling and decomposition temperature greater than 453K, and vapor pressure less than 0.0005 mmHg. Going beyond the space of known molecules, we apply our model with a generative approach to the CHNO chemical space, we discover 55,745 additional novel candidates with promising melt-castable characteristics. This three-orders-of-magnitude expansion highlights the power of coupling ML screening and generative design to accelerate materials discovery.