Large-scale multi-task regression and classification models for sustainability of molecular systems

New materials and molecules with improved properties are needed to continue advancing society in such fields as energy, space exploration, and electronics. Unfortunately, the typical process of discovery and characterization of new material is time-consuming, which has hindered the co-design of material and product or material and manufacturing. Predictive models using machine learning (ML) capable of forecasting the properties of new materials before they are synthesized have the potential to accelerate this process and enable co-design. However, the introduction of new materials is invariably a multi-objective process and needs to balance performance with sustainability metrics, including economic, environmental, and human health. Most ML efforts to accelerate discovery have focused on performance. This paper aggregates databases of toxicology, global warming potential (GWP), and fundamental physicochemical properties of molecules and uses the combined resource to train multi-task ML models. The models are trained for 617 classification and 30 regression metrics and outperform most current single-property models. Also, a chemical extraction study was performed using per- and polyfluoroalkyl substances to investigate the limits of our model when presented with molecules that are outside of the chemical space contained within our training data. We believe we have demonstrated that the data aggregation and models can accelerate the introduction of new materials in a wide range of applications.