Saifuddin Zafar | Searcharxiv

arXiv2024

Effect of Grain Size and Local Chemical Order on Creep Resistance in MoNbTaW Refractory High-Entropy Alloy: A Molecular Dynamics Study

Refractory high-entropy alloy (RHEA) is a promising class of materials with potential applications in extreme environments, where the dominant failure mode is thermal creep. The design of these alloys, therefore, requires an understanding of how their microstructure and local chemical distribution affect creep behavior. In this study, we performed high-fidelity atomistic simulations using machine-learning interatomic potentials to explore the creep deformation of MoNbTaW RHEA under a wide range of stress and temperature conditions. We parametrized grain size and local chemical order (LCO) to investigate the effects of these two important design variables, which can be controlled during the alloy fabrication process, on creep deformation process. Our investigation revealed that resistance to creep deformation is enhanced with larger grain size due to the reduced grain boundary area, which limits grain-boundary dominated deformation mechanisms such as Coble creep and grain boundary sliding. Introducing LCO in the microstructure has the same effect of increasing resistance to creep deformation by strengthening grain boundary. This study highlights the importance of utilizing LCO in conjunction with other microstructural properties when designing RHEAs for extreme environmental applications.

cond-mat.mtrl-sci↗

arXiv2024

Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys

Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-based representation for modeling medium-entropy alloys (MEAs). Hybrid Monte-Carlo molecular dynamics (MC/MD) simulations are employed to achieve thermally stable structures across various annealing temperatures in an MEA. These simulations generate dump files and potential energy labels, which are used to construct graph representations of the atomic configurations. Edges are created between each atom and its 12 nearest neighbors without incorporating explicit edge features. These graphs then serve as input for a Graph Convolutional Neural Network (GCNN) based ML model to predict the system's potential energy. The GCNN architecture effectively captures the local environment and chemical ordering within the MEA structure. The GCNN-based ML model demonstrates strong performance in predicting potential energy at different steps, showing satisfactory results on both the training data and unseen configurations. Our approach presents a graph-based modeling framework for MEAs and high-entropy alloys (HEAs), which effectively captures the local chemical order (LCO) within the alloy structure. This allows us to predict key material properties influenced by LCO in both MEAs and HEAs, providing deeper insights into how atomic-scale arrangements affect the properties of these alloys.

cond-mat.mtrl-sci↗