DeFecT-FF: Accelerated Modeling of Defects in Cd-Zn--Te-Se-S Compounds Combining High-Throughput DFT and Machine Learning Force Fields

TL;DR

DeFecT-FF is a novel framework that combines high-throughput DFT data with machine learning force fields to efficiently predict defect energies and configurations in Cd-Zn-Te-Se-S compounds, aiding solar cell material optimization.

Contribution

It introduces a publicly available, accelerated modeling framework that integrates active learning and MLFFs to predict defect properties in complex semiconductor alloys, reducing reliance on costly DFT calculations.

Findings

Accurately predicts defect energies across charge states

Enables rapid screening of defect configurations

Validated with high-level DFT calculations

Abstract

We developed DeFecT-FF, a framework for predicting the energies and ground-state configurations of native point defects, extrinsic dopants, impurities, and defect complexes in zincblende-phase Cd/Zn-Te/Se/S compounds relevant to CdTe-based solar cells. The framework combines high-throughput DFT data with crystal graph-based machine learning force fields (MLFFs) trained to reproduce DFT energies and forces. Alloying at Cd or Te sites offers a route to tune the electronic and defect properties of CdTe absorbers for improved solar efficiency. Given the vast number of possible defect types, charge states, and symmetry-breaking configurations, traditional DFT approaches are computationally prohibitive. Our dataset includes GGA-PBE and HSE06-optimized structures for bulk, alloyed, interface, and grain-boundary systems. Using active learning, we expanded the dataset and trained MLFFs to accurately predict energies across charge states. The model enabled rapid screening and discovery of new low-energy defect configurations, validated through HSE06 calculations with spin-orbit coupling. The DeFecT-FF framework is publicly available as a nanoHUB tool, allowing users to upload crystallographic files, automatically generate defects, and compute defect formation energies versus Fermi level and chemical potentials, greatly reducing the need for expensive DFT simulations.