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RESEARCH PAPER · Current Opinion in Solid State and Materials Science · 2022

Machine learning in nuclear materials research

Dane D. Morgan, Ghanshyam Pilania, Adrien Couet, Blas Pedro Uberuaga, Cheng Sun, Ju Li

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Machine learning in nuclear materials researchOriginalEML webinar overview: Elastic Strain Engineering for unprecedented properties9BatteryDataExtractor: battery-aware text-mining software embedded with BERT models43Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications3Learning constitutive relations of plasticity using neural networks and full-field data26An end-to-end computer vision methodology for quantitative metallography30Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels42Bi-objective optimization of thermal conductivity and thermal stress of UO2–Mo–Nb composite through Gaussian process regression and NSGA-II method4Deep learning object detection in materials science: Current state and future directions82Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study1Materials Genomics Search for Possible Helium‐Absorbing Nano‐Phases in Fusion Structural Materials5On the frontiers of coupled extreme environments3High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments11Application of LSTM Approach for Predicting the Fission Swelling Behavior within a CERCER Composite Fuel5Machine Learning and Artificial Intelligence-Driven Multi-scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications0Towards universal neural network interatomic potential69Data-driven predictive modeling of FeCrAl oxidation9Synthesis of atomically thin sheets by the intercalation-based exfoliation of layered materials253Rational design of high-entropy ceramics based on machine learning – A critical review52Autonomous and online detection of dry areas on a boiling surface using deep learning and infrared thermometry20Study of phase transition of Potts model with Domain Adversarial Neural Network9Materials swelling revealed through automated semantic segmentation of cavities in electron microscopy images27Robust deep learning framework for constitutive relations modeling31Probing radiation resistance in simulated metallic core–shell nanoparticles21Needs, Trends, and Advances in Scintillators for Radiographic Imaging and Tomography98Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels10Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning25CRESt – Copilot for Real-world Experimental Scientist6Autonomous experiments using active learning and AI61Elucidating precipitation in FeCrAl alloys through explainable AI: A case study22Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32An interpretable LSTM deep learning model predicts the time-dependent swelling behavior in CERCER composite fuels13Data-driven methods for diffusivity prediction in nuclear fuels14Can ChatGPT be used to generate scientific hypotheses?63Microstructure modeling of nuclear structural materials: Recent progress and future directions26Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions6Prediction of thermodynamic stability of actinide compounds by machine learning model13CRESt – Copilot for Real-world Experimental Scientist4CRESt – Copilot for Real-world Experimental Scientist2An approach to evaluate the accuracy of interatomic potentials as applied to tungsten22Radiation Anomaly Detection Using an Adversarial Autoencoder4CRESt – Copilot for Real-world Experimental Scientist9Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning27Retracted: Utilizing Machine Learning for Automated Real-Time Data Analysis Systems0Materials qualification through the Nuclear Science User Facilities (NSUF): a case study on irradiated PM-HIP structural alloys8A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel9An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel9Untitled work3Database for Properties of Nuclear Reactor Materials Based on the Ontology and NoSQL Data Format0Investigation of Primary Radiation Damage in Nanocrystalline Tantalum Using Machine-Learning Interatomic Potential: An Atomistic Simulation Study0Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach9Shallow Learning vs. Deep Learning in Engineering Applications13Towards informatics-driven design of nuclear waste forms11Prediction of two-phase flow patterns based on machine learning16Experimental and modeling study on irradiation effect of A508-Ⅲ steel9Machine learning method to determine concentrations of structural defects in irradiated materials8Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment45Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis3Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science19Nuclear fuel qualification: History, current state, and future13Materials Discovery0Bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance5Modelling and simulation of fusion materials0Recent machine learning-driven investigations into high entropy alloys: A comprehensive review37Machine Learning Prediction of Nitric Acid Extraction Behavior in PUREX Process7Roadmap for the development of machine learning-based interatomic potentials50Progress in radiation tolerant materials: Current insights from the perspective of grain size and environmental temperature10Coordination of the European Research Community on Nuclear Materials for Energy Innovation: the research agenda of the CONNECT-NM co-funded European partnership0Optimization of CO2 absorption rate for environmental applications and effective carbon capture22Machine learning approaches to natural fiber composites: A review of methodologies and applications20A Robust Anomaly Detection System for Nuclear Power Plants Under Varying Environmental Conditions and Malfunction Levels6Machine learning assessment of the importance of unirradiated yield strength as a variable in embrittlement trend forecasting7Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction33Recent Trends in Nuclear Accident Events Prediction: A Review10Insights into irradiation creep coefficient in nuclear graphite from machine learning5Machine Learning Models for Creep Rupture Prediction of Inconel 6172445High-throughput screening for novel medical materials: machine learning-enabled approaches0Comprehensive Toughness Dataset of Nuclear Reactor Structural Materials using Charpy V-Notch Impact Testing2Application of Machine Learning in Nano Research — A Review2Research advancements in machine learning-assisted design of reinforced composite radiation shields8A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research5Innovative applications of biochar in nuclear remediation and catalysis16The corrosion-related unidentified deposit (CRUD) and its multiple effects on boiling heat transfer in the nuclear reactor: A systematic review18Role of compositionally complex and high entropy alloys for hydrogen storage: A bibliometric and mechanistic assessment16Machine learning models for volumetric swelling in uranium nitride3Machine learning insights into the influence of Mg additives on MgCl2-NaCl-KCl molten salts: Deciphering structure and thermodynamic properties3A Critical Review of Machine Learning Analyses of Reactor Pressure Vessel Embrittlement and Hardening7Advanced spectroscopic, microscopic, and compositional techniques in catalytic material characterization: Applications and progress3A Comprehensive Overview of the Design Procedure, Bimodular Fracture Mechanics Analysis, and Potential Uses of Nuclear-Grade Graphite for Nuclear Applications1Investigating the corrosion transition mechanisms of zirconium alloys and the potential applications of machine learning in predictive modeling3Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning0Evaporation Behavior of Water in Confined Nanochannels Using Molecular Dynamics Simulation2Generalizable image segmentation for microstructure characterization through integrated SEM and EBSD analysis8Polymer composite scintillators: focus on fabrication methods, optical and scintillation properties4Machine Learning Applied to High Entropy Alloys under Irradiation8Low cost, mobile radiation anomaly detection with deep adversarial auto encoders on the edge1Research on the Variation Law of Resistivity of Composite Insulating Materials under Multiple Physical Fields0Neutron shielding: Advanced mechanisms, challenges, and material strategies9Augmented XGBoost modeling with CNN-extracted mass-spectral features and temporal embedding for enhanced prediction of pyrene degradation kinetics in advanced oxidation processes0Using machine learning to predict reactor pressure vessel embrittlement: Human factors and best practice2Immortal AI, space radiation, and materials science1Predicting Nuclear Level Density Using a Physics-Informed Neural Network with Multi-Task Learning2AI for Materials Development and Degradation Analysis in Automotive, Battery and Nuclear Industries0Automated Multimodal Knowledge Extraction from Materials Science Literature0Enhanced Growth and Bioproduction via Active Machine Learning-Guided Mammalian Cell Culture Media Optimization0Exploring the Mykonos Linear Transformer Driver Facility's Potential for Pulsed Power and High-Energy-Density Research0Nanostructured high-entropy oxides for catalysis: linking entropy to function21Producing Oxide Dispersion-Strengthened Fe-Cr Steel via Additive Manufacturing for Advanced Nuclear Reactors: Improved Radiation0Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning1Machine learning integrated higher-order model application for critical heat flux investigations in pressurized water reactors1Comprehensive review of neutron techniques, detection, and dosimetry in science and technology3A Machine learning method based on extended Kalman filter to predict critical heat flux (CHF) Occurring in narrow channels between fuel plates1Data-driven excess Xe reactivity modeling in CANDU nuclear reactor using machine learning methods0Accelerating thermochemistry in MOX fuel modelling via neural-network surrogates of CALPHAD calculations: Integration into SCIANTIX0Deep learning-based microcrack segmentation for damage evolution assessment in SiCf/SiC composites1Deep learning and X-ray computed tomography for 3D segmentation and damage characterization of SiCf/SiC composites1User-guided interactive machine learning for high-throughput, multiscale helium-bubble segmentation and quantification0An AI-enhanced model of athermal fission gas release in SCIANTIX2Interpretable artificial intelligence for materials science: A comprehensive review of methods, applications, and outlook0Controlling element segregation during the solidification process of nickel-based superalloys using pulsed electric current1Emotional and regulatory pathways linking instructional interaction to motivational engagement across educational levels0Thresholding and AI-based segmentation of TEM images for analyzing fission gas bubbles and metallic precipitates in irradiated UO20Data-driven deep learning model for critical heat flux occurrence prediction based on bubble dynamics in pressurized water reactors0Benchmarking and assessing models of reactor pressure vessel steel embrittlement0Revealing interaction mechanisms of substitutional and interstitial solutes in metals through machine learning0

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  4. Learning constitutive relations of plasticity using neural networks and full-field data

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  5. An end-to-end computer vision methodology for quantitative metallography

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  6. Intelligent disassembly of electric-vehicle batteries: a forward-looking overview

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  8. Bi-objective optimization of thermal conductivity and thermal stress of UO2–Mo–Nb composite through Gaussian process regression and NSGA-II method

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  9. Deep learning object detection in materials science: Current state and future directions

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  18. Synthesis of atomically thin sheets by the intercalation-based exfoliation of layered materials

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  46. A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

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  47. An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

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  49. Database for Properties of Nuclear Reactor Materials Based on the Ontology and NoSQL Data Format

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