Multifidelity Learning and Statistical Analysis of Material Properties

There are many examples of materials data where the coexistence of two types of datasets occur: a smaller, higher-fidelity (higher accuracy) dataset, and a larger, lower-fidelity dataset. Here, we investigate a statistical modeling approach for materials properties in these cases of varying fidelity. Kriging based multi-fidelity learning is an approach suited to these cases, and we demonstrate its usefulness by applying it to model and predict the chemical properties of compounds in the Open Quantum Materials Database (OQMD). Specifically, we focus datasets of DFT-calculated bandgaps, and consider a case of high-fidelity data from computationally expensive hybrid HSE calculations and low-fidelity PBE GGA calculations. The multifidelity model is trained on a database containing 1100 HSE bandgaps and combined with results from the OQMD which contains a much larger set of PBE bandgaps. We demonstrate the utility of this multifidelity approach in predicting HSE bandgaps for all materials in OQMD, analyze the predicted results for various material classes, and compare the multifidelity approach with multiple single-fidelity machine learning models trained on the same dataset.