About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Uncertainty Quantification in Data-Driven Materials and Process Design
|
Presentation Title |
Active Learning for Density Functional Theory Simulations with DeepHyper |
Author(s) |
Amit Samanta, Prasanna Balaprakash, Sylvie Aubry, Brian Lin |
On-Site Speaker (Planned) |
Brian Lin |
Abstract Scope |
Density Functional Theory (DFT) is used extensively in the materials science community to predict fundamental properties. However, DFT simulations are computationally intensive and costly, especially in the context of predicting properties for the development of new alloys. In this work, DFT was first applied to a binary Fe-Mn system to study the effect of Mn on the stacking fault energy. The outputs from DFT were fed into a neural network (NN) architecture search using the DeepHyper package that trains an ensemble of machine learning models, optimized using a genetic algorithm, to predict the configurational energy. The uncertainty associated with the predictions was quantified by DeepHyper, which was accomplished by not just perturbing the parameters of a single NN model, but by using an ensemble of NN models. In doing so, the uncertainty quantification estimates come without systematic bias inherent in methods that rely on a single NN model. |