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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title High-fidelity Accelerated Design of High-performance Electrochemical Systems
Author(s) Rachel C. Kurchin, Alan Edelman, Viral Shah, Chris Rackauckas, Bryce Meredig, Venkat Viswanathan
On-Site Speaker (Planned) Rachel C. Kurchin
Abstract Scope Two efforts to curb greenhouse gas emissions involve seeking an electrochemical alternative to the Haber-Bosch process and increasing energy density of lithium-ion batteries to enable electric aviation and trucking. Novel materials can address both these challenges; however, evaluations of candidate material systems in-silico and experimentally are limited to small design spaces and low-fidelity screening that fail to model realistic operating conditions. I will present a collaborative effort spanning academia and industry that aims to alter this paradigm by enabling rapid, high-fidelity screening of large numbers of electrochemical functional materials for use in new energy technologies. This approach will be demonstrated for a model problem in electrocatalysis and shown to extend for battery electrolyte design. We are advancing state-of-the-art along several fronts, including neural differential equations and graph convolutional neural networks for fast evaluation, and efficient global optimization over the chemical space. Experimental validation will be done for high-value candidates.


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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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