Developing a valence force field model for wurtzite semiconductors by exploiting similarities with [111]-oriented zinc blende systems: the case of wurtzite boron nitride, III-N materials and (B,In,Ga)N alloys

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Authors: Aisling Power, Cara‐Lena Nies, Stefan Schulz
Cited by: 0
References: 110

Abstract

Abstract Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for large scale simulations of heterostructures and their properties, relies heavily on valence force field (VFF) methods to find the equilibrium atomic positions in an alloy. For zinc blende crystals, VFF models are well-established. In the case of wurtzite, VFF parameters are frequently adopted without rigorous analysis, despite subtle but critical differences from the zinc blende structure. Such an approach can compromise accuracy in describing material properties, since the structural differences between zinc blende and wurtzite directly influence electronic and optical characteristics. Based on the analytical VFF model by Tanner et al (2019 Phys. Rev. B 100 094112), and using structural similarities between wurtzite and [111]-oriented zinc blende crystals, we guide the development of a wurtzite VFF without introducing additional parameters. Our framework utilizes analytic expressions and minimization routines to project zinc blende models onto wurtzite systems. Beyond elastic tensors, we train the model to reproduce bond length asymmetries and band gaps by using output of the VFF model in density functional theory (DFT) electronic structure calculations. Applied to wurtzite III-N compounds and BN, the model accurately reproduces targeted observables but also properties it has not been trained on, including the internal parameter u . We further validate the model on highly mismatched alloys such as (B,Ga)N and (B,In,Ga)N, exhibiting good agreement between VFF and DFT results when using identical supercells in these calculations.

Fields of Study & Subjects

Machine Learning in Materials Science GaN-based semiconductor devices and materials Boron and Carbon Nanomaterials Research

Publication details

Journal / source
Journal of Physics Condensed Matter
Publisher
IOP Publishing
Volume
38
Issue
21
Pages
215901–215901
Publication date
29 April 2026
Language
EN