Calibrating DFT formation enthalpy calculations by multi-fidelity machine learning

TL;DR

This paper introduces a multi-fidelity machine learning approach to calibrate DFT formation enthalpy calculations, improving accuracy over traditional functionals and uncovering materials with underestimated stability.

Contribution

It develops a multi-fidelity random forest model that surpasses existing methods in predicting experimental formation enthalpy and calibrates DFT data to identify stability discrepancies.

Findings

The model achieves higher prediction accuracy than PBEfe and SCAN.

It successfully calibrates DFT formation enthalpy in the Materials Project database.

The approach reveals materials with underestimated stability.

Abstract

Machine learning materials properties measured by experiments is valuable yet difficult due to the limited amount of experimental data. In this work, we use a multi-fidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the empirically corrected PBE functional (PBEfe) and meta-GGA functional (SCAN), and it outperforms the hotly studied deep neural-network based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database, and discover materials with underestimated stability. The multi-fidelity model is also used as a data-mining approach to find how DFT deviates from experiments by the explaining the model output.