Citing Articles For: Dynamic Vacancy Levels in Obey Equilibrium Defect Thermodynamics

Stability of Polarons and Oxygen Vacancies in BiVO4 Revealed by Machine-Learning-Driven Simulations

Ethan Berger, Tobias Hainer, Tobias Möslinger, Paul Erhart, and Julia Wiktor

PRX Energy 5 033004 (2026)

Multifidelity Machine Learning Interatomic Potentials for Charged Point Defects

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Predicting temperature-dependent optoelectronic properties of semiconductor defects with equivariant neural networks

Xiangzhou Zhu, Patrick Rinke, and David A. Egger

npj Comput Mater 12 176 (2026)

Guidelines for robust and reproducible point defect simulations in crystals

Alexander G. Squires, Seán R. Kavanagh, Aron Walsh, and David O. Scanlon

Nat Rev Mater 11 469 (2026)

Accelerating point-defect simulations using data-driven and machine learning approaches

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, and Seán R. Kavanagh

MRS Bulletin 51 600 (2026)

Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications

Ayush Kumar Pandey, Vivek Pandey, and Abhishek Tewari

Materials Today Physics 60 102003 (2026)

Dynamic chloride vacancy evolution enables thermally enhanced upconversion luminescence in Yb3+/Er3+ Co-doped Cs2NaBiCl6 double perovskites

Di Wu, Yuting Wu, Qi Wang, Zhiling Guo, Xinyue Yang, Zhangwen Long, Dacheng Zhou, Jingchang Lin, Tao Han, and Jianbei Qiu

Ceramics International 52 28367 (2026)

Medium-entropy perovskite quantum dots with both visible and infrared luminescence properties via solid-phase synthesis

Shuan Tang, Jiawen Luo, Yulu Zhao, Kehao Zhang, Cheng Fang, Mingliang Li, Gang Shao, Hailiang Wang, Hongliang Xu, and Hongxia Lu

Ceramics International 52 43756 (2026)

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

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adv. intell. discov. e202600007 (2026)

Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces

Tieyuan Bian, Wenjia Zhu, Qiong Lei, and Jun Yin

ACS Applied Materials & Interfaces 18 17219 (2026)

Deep Learning the Defect Landscape: Long-Timescale Vacancy Dynamics in Lead-Free Double Perovskites

Nikhil Singh, Vidhi Agarwal, and Dibyajyoti Ghosh

ACS Applied Energy Materials 9 13565 (2026)

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Articles Citing

Dynamic Vacancy Levels in ${Cs Pb Cl}_{3}$ Obey Equilibrium Defect Thermodynamics

Stability of Polarons and Oxygen Vacancies in BiVO4 Revealed by Machine-Learning-Driven Simulations

Multifidelity Machine Learning Interatomic Potentials for Charged Point Defects

Predicting temperature-dependent optoelectronic properties of semiconductor defects with equivariant neural networks

Guidelines for robust and reproducible point defect simulations in crystals

Accelerating point-defect simulations using data-driven and machine learning approaches

Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications

Dynamic chloride vacancy evolution enables thermally enhanced upconversion luminescence in Yb3+/Er3+ Co-doped Cs2NaBiCl6 double perovskites

Medium-entropy perovskite quantum dots with both visible and infrared luminescence properties via solid-phase synthesis

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces

Deep Learning the Defect Landscape: Long-Timescale Vacancy Dynamics in Lead-Free Double Perovskites

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