Summary We provide a review of machine learning (ML) tools for material discovery and sophisticated applications of different ML strategies. Although there have been a few published reviews on artificial intelligence (AI) for materials with an emphasis on a single material system or individual methods, this paper focuses on an application-based perspective in AI-enhanced material discovery. It shows how AI strategies are applied through material discovery stages (including characterization, property prediction, synthesis, and theory paradigm discovery). Also, by referring to the ML tutorial, readers can acquire a better understanding of the exact functions of ML methods in each application and how these methods work to realize the targets. We are aiming to enable a better integration of AI methods with the material discovery process. The keys to successful applications of AI in material discovery and challenges to be addressed are also highlighted.

AI Applications through the Whole Life Cycle of Material Discovery

The front-end of the nuclear fuel cycle encompasses several chemical and physical processes used to acquire and prepare uranium for use in a nuclear reactor. These same processes can also be used for weapons or nefarious purposes, necessitating the need for technical means to help detect, investigate, and prevent the nefarious use of nuclear material and nuclear fuel cycle technology. Over the past decade, a significant research effort has investigated uranium compounds associated with the front-end of the nuclear fuel cycle, including uranium ore concentrates (UOCs), UF4, UF6, and UO2F2. These efforts have furthered uranium chemistry with an aim to expand and improve the field of nuclear forensics. Focus has been given to the morphology of various uranium compounds, trace elemental and chemical impurities in process samples of uranium compounds, the degradation of uranium compounds, particularly under environmental conditions, and the development of improved or new techniques for analysis of uranium compounds. Overall, this research effort has identified relevant chemical and physical characteristics of uranium compounds that can be used to help discern the origin, process history, and postproduction history for a sample of uranium material. This effort has also identified analytical techniques that could be brought to bear for nuclear forensics purposes. Continued research into these uranium compounds should yield additional relevant chemical and physical characteristics and analytical approaches to further advance front-end nuclear fuel cycle forensics capabilities.

Progress in Uranium Chemistry: Driving Advances in Front-End Nuclear Fuel Cycle Forensics

Dynamic mechanical properties and fracturing behaviour of concrete under biaxial compression

Nuclear Forensics is a rapidly evolving field with a steadily increasing number of publications. Research in developing or consolidating signatures and methods in various analytical areas, such as elemental and isotopic parameters, age dating and morphology have resulted in significant progress. Moreover, the development of new reference materials for uranium ore concentrate, the application of micro-analytical techniques and the increasing use of data evaluation techniques such as machine learning or deep learning further add to the nuclear forensic toolbox. The examination of samples from real incidents demonstrate the usefulness and the investigative value of nuclear forensic techniques. 

Trends and perspectives in Nuclear Forensic Science

Thermoplastic vulcanizates (TPVs) could be extensively used in industrial fields due to their excellent processing performance and mechanical properties. Polymer processing, including the experimental formula and process technology, is the most direct and crucial factor that determines the performance of TPV. However, it is difficult to fully understand the relationship between polymer processing and property of TPV via the experimental means. Herein, a BP neural network model optimized by genetic algorithm has been designed via machine learning method. The R value of the prediction model could reach to 0.95, which could accurately predict the mechanical properties of different types of TPV. The impact of the important processing factors on the mechanical properties of TPV have been analyzed quantitatively via the bivariate partial dependence plots of TPV. This work could effectively guide the processing of TPV and optimize the properties of TPV. 

A predictive model for the relationship between processing conditions and properties of thermoplastic vulcanizates (TPVs) via machine learning

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. This review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics. 

Review of Multi-Faceted Morphologic Signatures of Actinide Process Materials for Nuclear Forensic Science

Learning to Estimate the Composition of a Mixture with Synthetic Data

In the last decade, Convolutional Neural Network (CNN) and transformer based object detectors have achieved high performance on a large variety of datasets. Though the majority of detection literature has developed this capability on datasets such as MS COCO, these detectors have still proven effective for remote sensing applications. Challenges in this particular domain, such as small numbers of annotated objects and low object density, hinder overall performance. In this work, we present a novel augmentation method, called collage pasting, for increasing the object density without a need for segmentation masks, thereby improving the detector performance. We demonstrate that collage pasting improves precision and recall beyond related methods, such as mosaic augmentation, and enables greater control of object density. However, we find that collage pasting is vulnerable to certain out-of-distribution shifts, such as image corruptions. To address this, we introduce two simple approaches for combining collage pasting with PixMix augmentation method, and refer to our combined techniques as ColMix. Through extensive experiments, we show that employing ColMix results in detectors with superior performance on aerial imagery datasets and robust to various corruptions.

ColMix - A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images

Determining the Composition of a Mixed Material with Synthetic Data

Determining uranium ore concentrates and their calcination products via image classification of multiple magnifications

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