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Meeting Materials Science & Technology 2020
Symposium Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Presentation Title SimuLearn: Machine Learning-empowered Fast and Accurate Simulator to Support 4D Printing Design
Author(s) Humphrey Yang, Kuanren Qian, Haolin Liu, Yuxuan Yu, Jianzhe Gu, Matthew McGehee, Yongjie Jessica Zhang, Lining Yao
On-Site Speaker (Planned) Humphrey Yang
Abstract Scope Recent technological advancements have created a library of smart materials that afford novel functionalities. 4D printing, in particular, administers more efficient and economical prototyping as well as manufacturing. However, due to the lack of fast and accurate transformation simulators, currently available 4D printing CAD tools cannot effectively support users to iterate designs that have complex topologies. To address this issue, we take mesh-like structures as an example to introduce a novel SimuLearn system that combines finite element analysis (FEA) and graph convolutional networks (GCN) to truthfully (97% accuracy versus FEA) inform design decisions in real-time (0.6 seconds) and deploy our implementation in a computational design tool to unveil the enabled design space. Results show that SimuLearn enables much faster design iteration and allows users to integrate material response into their design workflows. Beyond 4D printing, SimuLearn also enriches the computational toolbox for designing, engineering, and predicting smart, transformative materials.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Printing and Machine Learning
Cycle Life Prediction of Lithium Ion Batteries Based on Data Driven Methods
Expert-guided Learning for Data-constrained Materials Science Problems
Fast and Generalizable Detailed Router Using Attention-based Reinforcement Learning
Introductory Comments: Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Neural Network Potential for Lattice Dynamics Calculations and Thermal Conductivity Prediction
Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics
SimuLearn: Machine Learning-empowered Fast and Accurate Simulator to Support 4D Printing Design
Uncertainty Quantification and Active Learning of Neural Network Models for Predicting ZrO2 Crystal Energy

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