Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
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| Pubblicato in: | arXiv.org (Nov 20, 2024), p. n/a |
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| Autore principale: | |
| Altri autori: | , , , |
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Cornell University Library, arXiv.org
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| Accesso online: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3131951388 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3131951388 | ||
| 045 | 0 | |b d20241120 | |
| 100 | 1 | |a Mashaekh Tausif Ehsan | |
| 245 | 1 | |a Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys | |
| 260 | |b Cornell University Library, arXiv.org |c Nov 20, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Simulation | ||
| 653 | |a Medium entropy alloys | ||
| 653 | |a Configuration management | ||
| 653 | |a Thermal stability | ||
| 653 | |a Drawing | ||
| 653 | |a Potential energy | ||
| 653 | |a Material properties | ||
| 653 | |a Atomic structure | ||
| 653 | |a Modelling | ||
| 653 | |a Graph theory | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a High entropy alloys | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Graph representations | ||
| 653 | |a Neural networks | ||
| 653 | |a Molecular dynamics | ||
| 653 | |a Machine learning | ||
| 653 | |a Graphical representations | ||
| 653 | |a Entropy | ||
| 700 | 1 | |a Saifuddin Zafar | |
| 700 | 1 | |a Sarker, Apurba | |
| 700 | 1 | |a Sourav Das Suvro | |
| 700 | 1 | |a Mohammad Nasim Hasan | |
| 773 | 0 | |t arXiv.org |g (Nov 20, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3131951388/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2411.13670 |