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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Machine Learning Defect Properties of Semiconductors
Author(s) Arun Kumar Mannodi Kanakkithodi
On-Site Speaker (Planned) Arun Kumar Mannodi Kanakkithodi
Abstract Scope Defects and impurities in semiconductors heavily influence their performance in optoelectronic applications. Quick predictions of defect properties are desired in technologically important semiconductors, but complicated by difficulties in assigning measured levels to specific defects and by the expense of large-supercell first principles computations that involve charge corrections and advanced functionals. We address this issue by combining high-throughput density functional theory (DFT) with machine learning (ML) to develop predictive models for defect formation energies and charge transition levels, for three distinct defect datasets: (a) substitutional and interstitial impurities in zincblende semiconductors, (b) Pb-site doping in A(Pb)X3 hybrid perovskites, and (c) A/X vacancies in complex halide perovskite alloys. ML models combine unique encoding of the defect atom’s elemental properties, coordination environment, and unit cell defect data with rigorous training using random forests, Gaussian processes, and neural networks. DFT-ML datasets and models are made available as online tools for easy prediction and screening.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces

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