Calibrating DFT formation enthalpy calculations by multi-fidelity machine learning

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.

References in corpus (18)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• SchNet - a deep learning architecture for molecules and materials
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
• Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
• Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
• A critical examination of compound stability predictions from machine-learned formation energies
• Deep Neural Networks for Accurate Predictions of Garnet Stability
• Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data
• Calibrating transition metal energy levels and oxygen bands in first principles calculations: accurate prediction of redox potentials and charge transfer in lithium transition metal oxides
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
• Predicting charge density distribution of materials using a local-environment-based graph convolutional network
• Materials Representation and Transfer Learning for Multi-Property Prediction

Cited by in corpus (2)

• EC-MOF/Phase-I: A computationally ready database of electrically conductive metal-organic frameworks with high-throughput structural and electronic properties
• Efficient first principles based modeling via machine learning: from simple representations to high entropy materials