Accelerating Metal-Organic Framework Discovery via Synthesisability Prediction: The MFD Evaluation Method for One-Class Classification Models

Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class classification (OCC) approaches to accelerate the development of novel metal-organic framework materials by predicting their synthesisability. The evaluation of OCC model performance poses challenges, as traditional evaluation metrics are not applicable when dealing with a single type of data. To overcome this limitation, we introduce a quantitative approach, the Maximum Fractional Difference (MFD) method, to assess and compare model performance, as well as determine optimal thresholds for effectively distinguishing between positives and negatives. A DeepSVDD model with superior predictive capability is proposed. By combining assessment of synthetic viability with porosity prediction models, a list of 3,453 unreported combinations is generated characterised by predictions of high synthesisability and large pore size. The MFD methodology proposed in this study is intended to provide an effective complementary assessment method for addressing the inherent challenges in evaluating OCC models. The research process, developed models, and predicted results of this study are aimed at helping prioritisation of materials for synthesis.