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Meeting MS&T22: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Multi-Fidelity Machine Learning for Perovskite Discovery
Author(s) Arun Kumar Mannodi Kanakkithodi
On-Site Speaker (Planned) Arun Kumar Mannodi Kanakkithodi
Abstract Scope The ABX3 perovskite crystal structure is ubiquitous and the subject of extensive study owing to the sheer tunability of electronic and optical properties that can be achieved. The discovery of novel perovskite compositions, including complex alloys with attractive properties, is hindered by the combinatorial nature of the chemical space and a general lack of quantification of systematic inaccuracies in simulations such as from first principles density functional theory (DFT). In this work, we combine large datasets of DFT computed stability, band gaps, optical absorption, and defect formation energies of halide perovskites from various functionals with smaller quantities of corresponding experimental measurements, collected from the literature and generated at UCSD, and train multi-fidelity machine learning models to make property predictions at experimental accuracy. Such predictions are sequentially improved and coupled with a recommendation engine for new computations and experiments to gradually achieve new stable compositions with targeted band gap and absorption.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Feasibility Study of Machine Learning-assisted Alloy Design Using Wrought Aluminum Alloys as An Example
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Are Process-Structure-Property Relationships Useful for Materials Design?
A.I. Driven Sustainable Aluminum Alloy Design
B-1: Autonomous Closed Loop Synthesis of Gold Nanorods via a Modular Chemical-Handling Robotic Platform
B-2: Logistics Box Recognition in Robotic Industrial De-palletizing Procedure with Systematic RGB-D Image Processing Supported by Multiple Deep Learning Method
Data-driven Search for Promising Intercalating Ions and Layered Materials for Metal-ion Batteries
De Novo Inverse Design of Nanoporous Materials by Machine Learning
De Novo Molecular Drug Design Using Deep and Reinforcement Learning
Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
Deep Learning Approaches for Accelerating Polymer Characterization
Deep Neural Networks for Laser Absorptivity Prediction from Synchrotron X-ray Images
Estimation of Sub-micron Carbide Sizes and Morphologies in Dual-Phase Steels from Light Optical Micrographs Using Generative Adversarial Networks
High-dimensional Neural Network Potential for Liquid Electrolyte Simulations: Applications to Li-ion Battery Materials
Machine Learning Enabled Stacking Fault Energy Prediction in Concentrated Alloys
Machine Learning for Accelerated Defect Dynamics in Materials
Machine Learning Guided Prediction of Rupture Time of 347H Stainless Steel
Multi-Fidelity Machine Learning for Perovskite Discovery
Multi-property Graph Networks for Novel Materials Discovery
Neural Network Prediction of Dynamical Electron Back-Scattered Diffraction Patterns Based on Kinematical Patterns
Phase Identification by Neural Networks Trained from Experimental and Theoretical Structure Data
Physics-informed Machine Learning for Selected Area Electron Diffraction Data Analysis
Rapid Metallic Alloy Development Leveraging Machine Learning
Real-time and Large FOV Ptychography through AI@Edge
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