Arun Kumar Mannodi-Kanakkithodi

Assistant Professor

Purdue University

United States

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Articles (16)

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

Collaborators (6)

David P. Fenning

University of California

UNITED STATES

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Shijing Sun

University of Washington

UNITED STATES

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Maciej Polak

University of Wrocław

POLAND

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Mariana I. Bertoni

Arizona State University

UNITED STATES

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Michael Stuckelberger

Deutsches Elektronen-Synchrotron DESY

GERMANY

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Yujie Sun

Associate Professor

University of Cincinnati

UNITED STATES

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Arun Kumar Mannodi-Kanakkithodi

Research Interests

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Contact this professor

Articles (16)

Bismuth in Lead–Tin Alloy Perovskites: Effect on Material Properties and Photovoltaic Device Performance

A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning

Accelerating defect predictions in semiconductors using graph neural networks

Discovering novel halide perovskite alloys using multi-fidelity machine learning and genetic algorithm

Screening of novel halide perovskites for photocatalytic water splitting using multi-fidelity machine learning

Two-Dimensional Halide Pb-Perovskite–Double Perovskite Epitaxial Heterostructures

Defect engineering in ZnIn2X4 (X=S, Se, Te) semiconductors for improved photocatalysis

A framework for materials informatics education through workshops

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

A high-throughput computational dataset of halide perovskite alloys

Distribution of Copper States, Phases, and Defects across the Depth of a Cu-Doped CdTe Solar Cell

High-throughput computations and machine learning for halide perovskite discovery

A first principles investigation of ternary and quaternary II–VI zincblende semiconductor alloys

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

Deep learning the properties of inorganic perovskites

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Collaborators (6)

David P. Fenning

Shijing Sun

Maciej Polak

Mariana I. Bertoni

Michael Stuckelberger

Yujie Sun

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