ProgramMaster Logo
Conference Tools for MS&T21: Materials Science & Technology
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Author(s) Arun Kumar Mannodi Kanakkithodi, Maria K.Y. Chan
On-Site Speaker (Planned) Arun Kumar Mannodi Kanakkithodi
Abstract Scope Semiconductors with desirable electronic band structure and optical absorption are sought for solar cells, electronic devices, infrared sensors and quantum computing. In this work, we develop AI-based frameworks for the on-demand prediction of the phase stability, band gap, optical absorption spectra, photovoltaic figures of merit, dielectric constant, defect formation energies, and impurity energy levels in two broad classes of semiconductors, namely (a) halide perovskites, and (b) group IV, III-V and II-VI semiconductors. This framework is powered by high-throughput density functional theory (DFT) computations, unique encoding of the atom-composition-structure information, and rigorous training of advanced neural network-based predictive and optimization models. Multi-fidelity learning is applied to bridge the gap between (high quantities of) low accuracy calculations and (lower quantities of) accurate, expensive computations and experimental measurements. AI-based recommendations are synergistically coupled with targeted synthesis and characterization, leading to successful validation and discovery of novel compositions for improved performance in solar cells.
Proceedings Inclusion? Undecided


A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

Questions about ProgramMaster? Contact