| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Uncertainty Quantification in Data-Driven Materials and Process Design
|
| Presentation Title |
Neural Network Surrogate Predictions with Uncertainties for Materials Science |
| Author(s) |
Sam Reeve, Paul Laiu, Pei Zhang, Ying Yang, Dongwon Shin, Jong Youl Choi, Massimiliano Lupo Pasini, Dan Lu |
| On-Site Speaker (Planned) |
Sam Reeve |
| Abstract Scope |
Uncertainty bounds are a crucial part of making decisions from computational predictions; however, reliable and tractable methods of calculating those bounds are difficult and method-specific and therefore not regularly applied. In this work we combine the prediction intervals from three neural networks (PI3NN) approach, which avoids the substantial computational cost of ensemble-based uncertainty quantification (UQ), with two distinct data-driven neural network surrogates in materials science. First, we use the HydraGNN multi-task graph convolutional neural network to produce surrogate predictions for atomic systems with per-sample uncertainties from PI3NN. For multiple solid-state and molecular open-source datasets we highlight how the prediction intervals are calculated and further demonstrate the detection of regions with higher uncertainty outside the original training space. Similarly, with recently developed residual neural networks trained on CALPHAD data, we show similar results with the PI3NN method across both in-distribution and out of distribution data (in compositional space). |