Imperfections are not 0 K: free energy of point defects in crystals

Abstract

Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware, and the integration of new computational techniques such as machine learning, provide an opportunity to model longer length and time-scales than previously possible. In this Tutorial Review, we cover the description of free energies for defect formation at finite temperatures, including configurational (structural, electronic, spin) and vibrational terms. We discuss challenges in accounting for metastable defect configurations, progress such as machine learning force fields and thermodynamic integration to directly access entropic contributions, and bottlenecks in going beyond the dilute limit of defect formation. Such developments are necessary to support a new era of accurate defect predictions in computational materials chemistry.

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• Quantitative Modeling of Point Defects in $\beta$-Ga2O3 Combining Hybrid Functional Energetics with Semiconductor and Processes Thermodynamics
• Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics
• Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
• Accelerating point defect simulations using data-driven and machine learning approaches
• Tailoring the Electronic Properties of Monoclinic (InxAl1-x)2O3 Alloys via Substitutional Donors and Acceptors