ArticlePatterns (New York, N.Y.)2022
Universal machine learning framework for defect predictions in zinc blende semiconductors.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 46 citations in OpenAlex.
- Advances in quantum dot-based fluorescent biosensors for enzyme detection: mechanistic insights and applications in disease.Mikrochimica acta · 2026Review
- Nanometer Resolution Structure-Emission Correlation of Individual Quantum Emitters via Enhanced Cathodoluminescence in Twisted Hexagonal Boron Nitride.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Understanding Defect-Mediated Ion Migration in Semiconductors using Atomistic Simulations and Machine Learning.ACS materials Au · 2024Review
- High-throughput identification of spin-photon interfaces in silicon.Science advances · 2023Article
- A framework for materials informatics education through workshops.MRS bulletin · 2023Review
- Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities. Optimized kernel ridge, Gaussian process, random forest, and neural network regression models are applied to screen impurities with lower formation energy than dominant native defects in all compounds.
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