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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title AI-driven Discovery of Novel High Entropy Semiconductor Alloys
Author(s) Arun Kumar Mannodi Kanakkithodi, Xueying Li, David Fenning, Maria K.Y. Chan
On-Site Speaker (Planned) Arun Kumar Mannodi Kanakkithodi
Abstract Scope High-entropy alloys in semiconductor chemical spaces resulting from arbitrary mixing at cation or anion sites can help enhance the stability, optical absorption, electronic properties and performance of materials for applications such as solar cells, infrared and quantum sensors, and electronics. In this work, we develop a general AI-based framework for the on-demand prediction of the structure, formation energy, band gap, optical absorption and defect behavior of high entropy alloys belonging to semiconductor classes of interest in photovoltaic and related optoelectronic applications. This framework is powered by high-throughput quantum mechanical computations, unique descriptors ranging from atomic coordination environments to elemental properties to low-fidelity computational outputs, and the rigorous training of advanced neural network-based predictive and optimization models. AI-based recommendations are synergistically coupled with targeted synthesis and characterization, leading to the successful validation and discovery of novel compositions for improved performance in solar cells.


A Physics-informed AI Assistant for Atomic Layer Deposition
Accelerating the Discovery of New DP-steel Using Machine Learning-based Multiscale Materials Simulations
AI-driven Discovery of Novel High Entropy Semiconductor Alloys
Artificial Intelligence for Material and Process Design
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis
High-fidelity Accelerated Design of High-performance Electrochemical Systems
Investigating Crystallographic Texture Control Using Laser Powder-bed Fusion Additive Manufacturing
Learning Through Domain Knowledge: A Hierarchical Machine Learning Approach Towards the Prediction of Thermoplastic Polyurethane Properties
Machine Learning Prediction of Glass Properties Informed by Synthetic Data
MeltNet: Predicting alloy melting temperature by machine learning
Multi-information Source Batch Bayesian Optimization of Alloys
NEW - Polymer Property Prediction and Design through Multi-task Learning
Realistic 3D Microstructure Generation via Generative Adversarial Networks
Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
Text and Data Mining for Materials Synthesis

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