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

2023/01/01 by Irea Mosquera‐Lois, Seán R. Kavanagh, Johan Klarbring +2 · 1 voice · 53 citations
Engineering · Materials Science · Mathematics · #Advanced Semiconductor Detectors and Materials #Chemical physics #Condensed matter physics #Crystallographic defect #Energy (signal processing) #Engineering physics #Geometry #Ion-surface interactions and analysis #Machine Learning in Materials Science #Materials science #Mathematics #Nanotechnology #Physics #Point (geometry) #Quantum mechanics #Statistical physics

paper · pdf · doi:10.1039/d3cs00432e

published in Chemical Society Reviews 52(17), 5812-5826 (Royal Society of Chemistry)

openalex publication_date 2023/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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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Discussions

• Point defects in crystals act as molecules with distinct local structures & excitations. Following “Imperfections are not 0 K" doi.org/10.1039/D3CS..., Irea has trained ML force fields to explore the [bsky, 22 points, 0 comments]

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