Exploring Non-Toxic Green Refrigerants using Positive-Unlabeled Learning and High-Fidelity Search

Finding refrigerants is challenging because they should be non-toxic, non-flammable, energy-saving and thermodynamically stable. In addition, they should have low global warming potentials to mitigate global warming. Traditionally, a large number of molecules could be studied by quantum chemical calculations or experimental measurements. However, these methods are often challenging to apply on a large scale due to their time-consuming and resource-intensive nature. In this aspect, we propose a fully data-driven screening with machine learning models to find promising refrigerant candidates. We used two prediction methods: a less accurate but explicable feedforward neural network and a more accurate graph neural network (GNN). Using GNN, non-toxic molecules were determined using positive-unlabeled learning from the molecular dataset, which partially contains toxicity information. Out of 33k molecules, 7 molecules were recommended as future refrigerants. Molecular properties of these molecules and their uncertainties were also reported using data-driven methods. These contributed to the identification of alternative refrigerants, which remains a challenging topic today. We believe that our approach can be used to search for non-toxic emerging molecules in the future.