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1. Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks

   https://doi.org/10.1039/D0ME00020E  

   Wang, Yixing ; Zhang, Min ; Lin, Anqi ; Iyer, Akshay ; Prasad, Aditya Shanker ; Li, Xiaolin ; Zhang, Yichi ; Schadler, Linda S. ; Chen, Wei ; Brinson, L. Catherine ( June 2020 , Molecular Systems Design & Engineering)

   Data-driven methods have attracted increasingly more attention in materials research since the advent of the material genome initiative. The combination of materials science with computer science, statistics, and data-driven methods aims to expediate materials research and applications and can utilize both new and archived research data. In this paper, we present a data driven and deep learning approach that builds a portion of the structure–property relationship for polymer nanocomposites. Analysis of archived experimental data motivates development of a computational model which allows demonstration of the approach and gives flexibility to sufficiently explore a wide range of structures. Taking advantage of microstructure reconstruction methods and finite element simulations, we first explore qualitative relationships between microstructure descriptors and mechanical properties, resulting in new findings regarding the interplay of interphase, volume fraction and dispersion. Then we present a novel deep learning approach that combines convolutional neural networks with multi-task learning for building quantitative correlations between microstructures and property values. The performance of the model is compared with other state-of-the-art strategies including two-point statistics and structure descriptor-based approaches. Lastly, the interpretation of the deep learning model is investigated to show that the model is able to capture physical understandings while learning. 

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2. Influence of Degree of Severe Plastic Deformation on Thermal Stability of an HfNbTiZr Multi-Principal Element Alloy Processed by High-Pressure Torsion

   https://doi.org/10.3390/nano12193371  

   Hung, Pham Tran ; Kawasaki, Megumi ; Szabó, Ábel ; Lábár, János L. ; Hegedűs, Zoltán ; Gubicza, Jenő ( October 2022 , Nanomaterials)

   Severe plastic deformation (SPD) is an effective route for the nanocrystallization of multi-principal element alloys (MPEAs). The stability of the refined microstructure is important, considering the high temperature applications of these materials. In the present study, the effect of SPD on the stability of a body-centered cubic (bcc) HfNbTiZr MPEA was investigated. SPD was performed using a high-pressure torsion (HPT) technique by varying the number of turns between ½ and 10. The evolution of phase composition and microstructure was studied near the disk centers and edges where the imposed strain values were the lowest and highest, respectively. Thus, the shear strain caused by HPT varies between 3 (½ turn, near the center) and 340 (10 turns, near the edge). It was found that during annealing up to 1000 K, the bcc HfNbTiZr alloy decomposed into two bcc phases with different lattice constants at 740 K. In addition, at high strains a hexagonal close packed (hcp) phase was formed above 890 K. An inhomogeneous elemental distribution was developed at temperatures higher than 890 K due to the phase decomposition. The scale of the chemical heterogeneities decreased from about 10 µm to 30 nm where the shear strain increased from 3 to 340, which is similar to the magnitude of grain refinement. Anneal-induced hardening was observed in the MPEA after HPT for both low and high strains at 740 K, i.e., the hardness of the HPT-processed samples increased due to heat treatment. At low strain, the hardness remained practically unchanged between 740 and 1000 K, while for the alloy receiving high strains there was a softening in this temperature range. 

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3. Deep learning approach for chemistry and processing history prediction from materials microstructure

   https://doi.org/10.1038/s41598-022-08484-7  

   Farizhandi, Amir Abbas ; Betancourt, Omar ; Mamivand, Mahmood ( December 2022 , Scientific Reports)

   Abstract Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe–Cr–Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe–Cr–Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth. 

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4. Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential

   https://doi.org/10.1186/s41313-021-00038-0  

   Attari, Vahid ; Arroyave, Raymundo ( January 2022 , Materials Theory)

   Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu₆Sn₅intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rankσ_SI,σ_IL, andσ_IL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσ_SIandσ_SLalter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

    

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5. Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

   https://doi.org/10.1002/prep.202200276  

   Choi, Joseph B. ; Nguyen, Phong C. H. ; Sen, Oishik ; Udaykumar, H. S. ; Baek, Stephen ( March 2023 , Propellants, Explosives, Pyrotechnics)

   Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out “process” as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research.

    

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