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Meeting Materials Science & Technology 2020
Symposium Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
Presentation Title Uncertainty Quantification and Active Learning of Neural Network Models for Predicting ZrO2 Crystal Energy
Author(s) Jayanth Koushik, Sungjun Choi, Aarti Singh
On-Site Speaker (Planned) Jayanth Koushik
Abstract Scope Neural networks are increasingly being used to model complex functions that arise naturally in material science; networks trained to predict crystal energies can avoid prohibitively expensive computations. However, it is challenging to analyze predictions of neural networks to guide further analysis because the prediction mechanism is poorly understood. One issue is obtaining uncertainty estimates of predictions, which can be used to identify abnormal data points, or adaptively sample additional points. We present a novel algorithm to efficiently approximate predictive variances of neural networks. Our method uses the same idea as the Jackknife method from statistics, but avoids any re-training, making it scalable to large datasets. We apply our method on a network trained to predict energy of ZrO2 crystals, and successfully identify mislabeled and abnormal structures in the data set. We also demonstrate improved performance in training the network actively, when points are sampled based on uncertainty rather than randomly.

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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