arXiv subjects

Mashaekh Tausif Ehsan

Publications and source records attributed to Mashaekh Tausif Ehsan.

4 recordsLinked to original sources

arXiv2026

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↗

arXiv2025

Atomistic insights into Cu segregation effects on irradiation-induced defect dynamics in medium-entropy alloys

Copper (Cu) segregation in medium and high-entropy alloys (M/HEAs) has shown significant influence on alloy properties. In this study, we investigate the effect of Cu segregation on evolution of irradiation-induced defects in FeNiCu, a model MEA, using hybrid molecular dynamics (MD) and Monte Carlo (MC) simulations. Thermodynamically driven hybrid MC/MD annealing at low temperature resulted in a partially decomposed Cu-segregated structure (CSS) and was compared with a random solid solution (RSS) and pure Ni. Results through cumulative displacement cascades reveal that Cu-rich domains in CSS act as defect traps, accelerating interstitial-vacancy recombination and suppressing defect cluster growth. The complex potential energy landscape (PEL) in CSS disrupts dislocation propagation, leading to spatially dispersed networks. Notably, CSS exhibits reduced stair-rod dislocation density compared to RSS, highlighting its superior resistance to irradiation swelling. Localized shear strain causes dislocations to preferentially nucleate in/near Cu-rich regions though their growth is hindered by chemical heterogeneity of the alloy. Notably, prolonged irradiation induces slow Cu segregation in the RSS structure, while slowly annihilating pre-existing Cu clusters in CSS simultaneously. The findings provide atomic-scale insights into the interplay between Cu segregation and irradiation-induced defect evolution in MEAs.

cond-mat.mtrl-sci↗

arXiv2024

G-RAG: Knowledge Expansion in Material Science

In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context constraints, and inaccurate retrieval. To address these issues, Graph RAG integrates graph databases to enhance the retrieval process. Our proposed method processes Material Science documents by extracting key entities (referred to as MatIDs) from sentences, which are then utilized to query external Wikipedia knowledge bases (KBs) for additional relevant information. We implement an agent-based parsing technique to achieve a more detailed representation of the documents. Our improved version of Graph RAG called G-RAG further leverages a graph database to capture relationships between these entities, improving both retrieval accuracy and contextual understanding. This enhanced approach demonstrates significant improvements in performance for domains that require precise information retrieval, such as Material Science.

cs.IR↗

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↗