Recent research on two-dimensional anion exchange membranes has highlighted the potential of MXene-based anion exchange membranes in advanced applications. This review provides a comprehensive summary...

Numerical simulation is a vital tool in the development of FeCrAl alloy cladding tubes, with its reliability closely tied to the predictive accuracy of the thermal deformation constitutive model used. In this study, hot compression tests on 0Cr23Al5 alloy were conducted using a Gleeble-3800 thermal compression testing machine (Dynamic Systems Inc., located in Albany, NY, USA), across a temperature range of 850–1050 °C and a strain rate range of 0.1–10 s⁻¹. Based on the data obtained, both the Arrhenius constitutive model and the artificial neural network (ANN) model were developed. The ANN model demonstrated significantly superior predictive accuracy, with an average absolute relative error (AARE) of only 0.70% and a root mean square error (RMSE) of 1.99 MPa, compared to the Arrhenius model (AARE of 4.30% and RMSE of 14.47 MPa). Further validation via the VUHARD user subroutine in ABAQUS revealed that the ANN model has good applicability and reliability in numerical simulations, with its predicted flow stress showing high consistency with the experimental data. The ANN model developed in this study can effectively predict the rheological stress of FeCrAl alloys during hot deformation. It provides methodological support for high-fidelity constitutive modeling of the flow stress of FeCrAl alloys and offers a reliable constitutive model for simulating the thermomechanical load response behavior of FeCrAl alloys.

The ability of AI-based algorithms to reflect the physical properties of training data and accurately predict the properties of undeveloped materials has made artificial intelligence (AI) algorithms an important tool in the domain of materials science. Material structure design, as a multi-step and multi-scientific task, involves many aspects from the determination of design objectives to material selection, preparation methods, sample testing and performance evaluation, etc. Traditional experiment-driven, theory-driven and algorithm-driven approaches have accumulated a large amount of textual material text data. With the development of technology, the data-driven approach of “Big Data+AI” has accelerated the development of material structure design, and in particular, deep learning (DL) as a branch of machine learning has become the fastest growing topic in materials science because of its powerful capabilities to analyze unstructured data and automatically identify features. In this paper, we focus on the common deep learning methods in materials research, and then review the material property prediction, material structure optimization, material discovery and information extraction from materials literature in the context of deep learning-based material structure design. At the same time, we introduce the hardware acceleration technologies based on deep learning. Finally, the summary and future directions of deep learning in future materials research are discussed.

One of the primary goals and challenges in catalytic applications is the identification of highly efficient catalysts. Traditionally, this issue has been addressed through experimental “trial-and-erro...

Artificial intelligence (AI), as an important driving force of the technological revolution, is changing the way humans produce, live, and learn. In recent years, massive training data, advanced al...