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

TL;DR

This paper reviews recent advances in modeling the free energies of point defects in crystals at finite temperatures, emphasizing computational techniques like machine learning and thermodynamic integration to improve defect predictions.

Contribution

It provides a comprehensive overview of current methods and challenges in calculating defect formation free energies, highlighting recent progress in computational approaches.

Findings

Inclusion of vibrational and configurational entropy improves defect energy predictions.

Machine learning force fields enable longer and larger-scale defect simulations.

Addressing metastable defect states is crucial for accurate defect thermodynamics.

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.