Revealing the structure-property relationships of copper alloys with FAGC

• URL: http://arxiv.org/abs/2404.09515v2
• Date: Fri, 19 Apr 2024 01:43:56 GMT
• Title: Revealing the structure-property relationships of copper alloys with FAGC
• Authors: Yuexing Han, Guanxin Wan, Tao Han, Bing Wang, Yi Liu,
• Abstract summary: We introduce a method known as FAGC (Feature Augmentation on Geodesic Curves), specifically demonstrated for Cu-Cr-Zr alloys. This approach utilizes machine learning to examine the shapes within images of the alloys' microstructures and predict their mechanical and electronic properties. Our FAGC method has shown remarkable results, significantly improving the accuracy of predicting the electronic conductivity and hardness of Cu-Cr-Zr alloys.
• Score: 7.00651980770986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding how the structure of materials affects their properties is a cornerstone of materials science and engineering. However, traditional methods have struggled to accurately describe the quantitative structure-property relationships for complex structures. In our study, we bridge this gap by leveraging machine learning to analyze images of materials' microstructures, thus offering a novel way to understand and predict the properties of materials based on their microstructures. We introduce a method known as FAGC (Feature Augmentation on Geodesic Curves), specifically demonstrated for Cu-Cr-Zr alloys. This approach utilizes machine learning to examine the shapes within images of the alloys' microstructures and predict their mechanical and electronic properties. This generative FAGC approach can effectively expand the relatively small training datasets due to the limited availability of materials images labeled with quantitative properties. The process begins with extracting features from the images using neural networks. These features are then mapped onto the Pre-shape space to construct the Geodesic curves. Along these curves, new features are generated, effectively increasing the dataset. Moreover, we design a pseudo-labeling mechanism for these newly generated features to further enhance the training dataset. Our FAGC method has shown remarkable results, significantly improving the accuracy of predicting the electronic conductivity and hardness of Cu-Cr-Zr alloys, with R-squared values of 0.978 and 0.998, respectively. These outcomes underscore the potential of FAGC to address the challenge of limited image data in materials science, providing a powerful tool for establishing detailed and quantitative relationships between complex microstructures and material properties.

Related papers

• Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features [0.2692359362045324]
  It is crucial to develop a data-reduced representation of metal microstructures. This need is particularly relevant for metallic materials processed through additive manufacturing. We propose the physical spatial mapping of metal diffraction latent space features.
  arXiv  Detail & Related papers  (2025-01-30T00:16:07Z)
• Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys [0.0]
  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. These graphs then serve as input for a Graph Convolutional Neural Network (GCNN) based ML model to predict the system's potential energy.
  arXiv  Detail & Related papers  (2024-11-20T19:22:40Z)
• Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning [8.523289773617503]
  We build an element property knowledge graph and utilize an embedding model to encode the element attributes within the knowledge graph. A multimodal fusion framework, ESNet, integrates element property features with crystal structure features to generate joint multimodal representations. This provides a more comprehensive perspective for predicting the performance of crystalline materials.
  arXiv  Detail & Related papers  (2024-11-13T08:07:21Z)
• Deep Learning-Driven Microstructure Characterization and Vickers Hardness Prediction of Mg-Gd Alloys [7.246224582503583]
  This study proposes a multimodal fusion learning framework based on image processing and deep learning techniques. It integrates elemental composition and microstructural features to accurately predict the Vickers hardness of solid-solution Mg-Gd alloys.
  arXiv  Detail & Related papers  (2024-10-27T10:28:29Z)
• Compositional Representation of Polymorphic Crystalline Materials [56.80318252233511]
  We introduce PCRL, a novel approach that employs probabilistic modeling of composition to capture the diverse polymorphs from available structural information. Extensive evaluations on sixteen datasets demonstrate the effectiveness of PCRL in learning compositional representation.
  arXiv  Detail & Related papers  (2023-11-17T20:34:28Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Formula graph self-attention network for representation-domain independent materials discovery [3.67735033631952]
  We introduce a new concept of formula graph which unifies both stoichiometry-only and structure-based material descriptors. We develop a self-attention integrated GNN that assimilates a formula graph and show that the proposed architecture produces material embeddings transferable between the two domains. Our model substantially outperforms previous structure-based GNNs as well as structure-agnostic counterparts.
  arXiv  Detail & Related papers  (2022-01-14T19:49:45Z)
• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning [82.67551367327634]
  We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
  arXiv  Detail & Related papers  (2021-11-10T21:19:02Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.