Revealing the structure-property relationships of copper alloys with FAGC

• URL: http://arxiv.org/abs/2404.09515v3
• Date: Thu, 06 Nov 2025 02:33:05 GMT
• Title: Revealing the structure-property relationships of copper alloys with FAGC
• Authors: Yuexing Han, Ruijie Li, Guanxin Wan, Gan Hu, Yi Liu, Bing Wang,
• Abstract summary: Cu-Cr-Zr alloys play a crucial role in electronic devices and the electric power industry.<n>Due to the scarcity of available samples, there has been a lack of effective studies exploring the relationship between the microstructural images of Cu-Cr-Zr alloys and their key properties.<n>The FAGC feature augmentation method is employed to enhance the microstructural images of Cu-Cr-Zr alloys within a feature space known as the pre-shape space.
• Score: 9.444321951789105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cu-Cr-Zr alloys play a crucial role in electronic devices and the electric power industry, where their electrical conductivity and hardness are of great importance. However, due to the scarcity of available samples, there has been a lack of effective studies exploring the relationship between the microstructural images of Cu-Cr-Zr alloys and their key properties. In this paper, the FAGC feature augmentation method is employed to enhance the microstructural images of Cu-Cr-Zr alloys within a feature space known as the pre-shape space. Pseudo-labels are then constructed to expand the number of training samples. These features are then input into various machine learning models to construct performance prediction models for the alloy. Finally, we validate the impact of different machine learning methods and the number of augmented features on prediction accuracy through experiments. Experimental results demonstrate that our method achieves superior performance in predicting electrical conductivity (\(R^2=0.978\)) and hardness (\(R^2=0.998\)) when using the decision tree classifier with 100 augmented samples. Further analysis reveals that regions with reduced image noise, such as fewer grain or phase boundaries, exhibit higher contributions to electrical conductivity. These findings highlight the potential of the FAGC method in overcoming the challenges of limited image data in materials science, offering a powerful tool for establishing detailed and quantitative relationships between complex microstructures and material properties.

Related papers

• Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes [9.342098489571326]
  Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase boundaries and inter-particle connections strongly influence system performance.<n>Here, we develop a machine learning (ML) enabled framework that enables automated transformation of experimental multimodal X-ray images of multiphase particulate composites into scalable, topology-aware graphs for extracting physical insights and establishing local microstructure-property relationships at both the particle and network level.
  arXiv  Detail & Related papers  (2025-12-18T02:00:22Z)
• Data Driven Insights into Composition Property Relationships in FCC High Entropy Alloys [28.495739557732175]
  Structural High Entropy Alloys (HEAs) are crucial in advancing technology across various sectors.<n>The scarcity of integrated chemistry, process, structure, and property data presents significant challenges for predictive property modeling.<n>This work presents several sensitivity analyses, highlighting key elemental contributions to mechanical behavior.
  arXiv  Detail & Related papers  (2025-08-06T19:41:15Z)
• Tokenizing Electron Cloud in Protein-Ligand Interaction Learning [51.74909649330779]
  ECBind is a method for tokenizing electron cloud signals into quantized embeddings.<n>It helps uncover binding modes that cannot be fully represented by atom-level models.<n>To extend its applicability to a wider range of scenarios, we utilize knowledge distillation to develop an electron-cloud-agnostic prediction model.
  arXiv  Detail & Related papers  (2025-05-25T07:36:50Z)
• Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
  It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery. Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs) By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
  arXiv  Detail & Related papers  (2025-03-18T02:14:49Z)
• Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers [0.0]
  This study integrates the analysis of diverse data types (multimodal data) using factor analysis for feature extraction with Explainable AI (XAI)<n>This method is a powerful approach to elucidate the mechanisms governing material properties, where multi-stage fabrication conditions and multiscale structures have complex influences.
  arXiv  Detail & Related papers  (2025-02-11T09:29:23Z)
• 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)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  We propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential.<n>Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations.<n>Our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• 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)
• Exploring structure diversity in atomic resolution microscopy with graph neural networks [18.903519247639355]
  Deep learning is a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner.
  arXiv  Detail & Related papers  (2024-10-23T07:48:35Z)
• 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)
• Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
  We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
  arXiv  Detail & Related papers  (2023-10-07T21:36:55Z)
• DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials [0.0]
  We propose DeepCrysTet, a novel deep learning approach for predicting material properties. DeepCrysTet uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. Experiments show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and state-of-the-art performance in predicting elastic properties.
  arXiv  Detail & Related papers  (2023-09-07T05:23:52Z)
• 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)
• Microscopic Relaxation Channels in Materials for Superconducting Qubits [76.84500123816078]
  We show correlations between $T_$ and grain size, enhanced oxygen diffusion along grain boundaries, and concentration of suboxides near the surface. Physical mechanisms connect these microscopic properties to residual surface resistance and $T_$ through losses arising from the grain boundaries and from defects in the suboxides. This comprehensive approach to understanding qubit decoherence charts a pathway for materials-driven improvements of superconducting qubit performance.
  arXiv  Detail & Related papers  (2020-04-06T18:01:15Z)

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.