Machine learning in nuclear materials research

Abstract

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeup, with multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that based on Gaussian process and Bayesian optimization. We show ML examples in structural materials ( e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. This review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends in artificial intelligence (AI) and machine learning will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

References in corpus (26)

• Generative Adversarial Networks: An Overview
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• ARC: A compact, high-field, fusion nuclear science facility and demonstration power plant with demountable magnets
• Opportunities and Challenges for Machine Learning in Materials Science
• Identifying structural flow defects in disordered solids using machine learning methods
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• Deep Learning for Generic Object Detection: A Survey
• A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
• Paradigm shift in electron-based crystallography via machine learning
• Machine-learning interatomic potential for radiation damage and defects in tungsten
• Quantum Interface of an Electron and a Nuclear Ensemble
• Stable Three-Axis Nuclear Spin Gyroscope in Diamond
• Coherent electrical control of a single high-spin nucleus in silicon
• TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations
• Conceptual design study for heat exhaust management in the ARC fusion pilot plant
• Ultra-uniform Nanocrystalline Materials via Two-Step Sintering
• Proton Irradiation-Decelerated Intergranular Corrosion of Ni-Cr Alloys in Molten Salt
• Competing dynamics of single phosphorus dopant in graphene with electron irradiation
• A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
• Transient grating spectroscopy: An ultrarapid, nondestructive materials evaluation technique
• Multi defect detection and analysis of electron microscopy images with deep learning
• Retrieving the quantitative chemical information at nanoscale from SEM EDX measurements by Machine Learning
• Automated Classification of Helium Ingress in Irradiated X-750
• MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach
• Accelerated Discovery of Molten Salt Corrosion-resistant Alloy by High-throughput Experimental and Modeling Methods Coupled to Data Analytics

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• Study of phase transition of Potts model with Domain Adversarial Neural Network
• Machine learning method to determine concentrations of structural defects in irradiated materials
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