Mosquera-Lois Irea, Kavanagh Seán R, Klarbring Johan, Tolborg Kasper, Walsh Aron
Thomas Young Centre & Department of Materials, Imperial College London, London SW7 2AZ, UK.
Thomas Young Centre & Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK.
Chem Soc Rev. 2023 Aug 29;52(17):5812-5826. doi: 10.1039/d3cs00432e.
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.
缺陷决定了材料的许多重要性质和应用，范围涵盖从半导体中的掺杂、电池中使用的混合离子 - 电子导体的导电性，到催化剂中的活性位点。在过去的一个世纪里，晶体中缺陷形成的理论描述有了很大的发展。超级计算硬件的进步以及机器学习等新计算技术的整合，提供了一个机会来模拟比以前更长的长度和时间尺度。在本教程综述中，我们涵盖了有限温度下缺陷形成自由能的描述，包括构型（结构、电子、自旋）和振动项。我们讨论了在考虑亚稳态缺陷构型方面的挑战、诸如机器学习力场和热力学积分等直接获取熵贡献的进展，以及超越缺陷形成稀溶液极限的瓶颈。这些发展对于支持计算材料化学中精确缺陷预测的新时代是必要的。
