An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Abstract

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with about 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers about 20% shorter training time without any loss of accuracy.

References in corpus (7)

• Theia: An advanced optical neutrino detector
• Machine Learning and Big Scientific Data
• Machine learning and density functional theory
• Machine Learning Assisted Insight to Spin Ice Dy${}_2$Ti${}_2$O${}_7$
• Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
• Active learning-assisted neutron spectroscopy with log-Gaussian processes
• Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation