Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning
Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao‐Horn, Jeffrey C. Grossman
Abstract
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 multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.
Topics & Concepts
Stability (learning theory)EnthalpyRepresentation (politics)Artificial neural networkWork (physics)Computer scienceDensity functional theoryMachine learningExperimental dataArtificial intelligenceStandard enthalpy change of formationThermodynamicsAlgorithmStatistical physicsChemistryMathematicsComputational chemistryPhysicsStatisticsLawPoliticsPolitical scienceMachine Learning in Materials ScienceX-ray Diffraction in CrystallographyComputational Drug Discovery Methods