Citing Articles For: Machine-Learning Prediction of Charged-Defect Formation Energies from Crystal Structures

Multifidelity Machine Learning Interatomic Potentials for Charged Point Defects

Xinwei Wang, Irea Mosquera-Lois, and Aron Walsh

PRX Energy 5 043001 (2026)

Active-learning inspired ab initio theory-experiment loop approach for management of material defects: Application to superconducting qubits

Sarvesh Chaudhari, Cristóbal Méndez, Rushil Choudhary, Tathagata Banerjee, Maciej W. Olszewski, Jadrien T. Paustian, Jaehong Choi, Zhaslan Baraissov, Raul Hernandez, David A. Muller, B. L. T. Plourde, Gregory D. Fuchs, Valla Fatemi, and Tomás A. Arias

Phys. Rev. Materials 10 L040801 (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 and first-principles calculations for electronic and transport properties in p-type Cd-doped two-dimensional Ga2O3

H Zeng, W Tang, C Ma, X Fu, Y R Xue, and M Wu

J. Phys. D: Appl. Phys. 59 425105 (2026)

Why GNNs Fail for Metastable Phase Formation Energies: A Failure Mode Analysis

Kevin O’Brien, Liam Murphy, and Sean Doyle

J. Comput. Data-Driven Mater. Eng. 5 (2026)

Interplay of Hubbard localization and neural learning dynamics in defect-engineered cerium oxides

Saswati Bagchi and D. Vignesh

Computational Materials Science 273 114932 (2026)

Oxygen-Deficiency-Driven Phase Segregation Enables Enhanced Hole Transport in Amorphous Tellurium Oxides

Rafael Costa-Amaral and Yu Kumagai

Chem. Mater. 38 6006 (2026)

Machine learning approaches to point defects in non-metallic materials: a review of methods

Yu Kumagai and Shin Kiyohara

Appl. Phys. Express 19 060101 (2026)

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

Akira Takahashi, Yu Kumagai, Arata Takamatsu, and Fumiyasu Oba

adv. intell. discov. e202600007 (2026)

Machine-Learning-Driven Discovery of Water Splitting BaFe2O4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Arielle Clauser, Keith King, Daniel Lowry, Tyra Douglas, Amanda Peretti, Andrew Smith, Andrew J. E. Rowberg, Joel B. Varley, Stephan Lany, Nicholas A. Strange, Matthew D. Witman, Anthony H. McDaniel, Sean Bishop, and Joshua D. Sugar

ACS Appl. Energy Mater. 9 5765 (2026)

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Articles Citing

Machine-Learning Prediction of Charged-Defect Formation Energies from Crystal Structures

Multifidelity Machine Learning Interatomic Potentials for Charged Point Defects

Active-learning inspired ab initio theory-experiment loop approach for management of material defects: Application to superconducting qubits

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

Machine learning and first-principles calculations for electronic and transport properties in p-type Cd-doped two-dimensional Ga2O3

Why GNNs Fail for Metastable Phase Formation Energies: A Failure Mode Analysis

Interplay of Hubbard localization and neural learning dynamics in defect-engineered cerium oxides

Oxygen-Deficiency-Driven Phase Segregation Enables Enhanced Hole Transport in Amorphous Tellurium Oxides

Machine learning approaches to point defects in non-metallic materials: a review of methods

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-Driven Discovery of Water Splitting BaFe2O4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

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