Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design

• URL: http://arxiv.org/abs/2409.02648v1
• Date: Wed, 4 Sep 2024 12:26:19 GMT
• Title: Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design
• Authors: Xudong Ma, Yuqi Zhang, Chenchong Wang, Ming Wang, Mingxin Huang, Wei Xu,
• Abstract summary: This study introduces an improved alloy design algorithm that integrates authentic microstructural information to establish precise CPSP relationships. The approach utilizes a deep-learning framework based on a variational autoencoder to map real microstructural data to a latent space.
• Score: 19.10106845551149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The intricate microstructure serves as the cornerstone for the composition/processing-structure-property (CPSP) connection in multiphase alloys. Traditional alloy design methods often overlook microstructural details, which diminishes the reliability and effectiveness of the outcomes. This study introduces an improved alloy design algorithm that integrates authentic microstructural information to establish precise CPSP relationships. The approach utilizes a deep-learning framework based on a variational autoencoder to map real microstructural data to a latent space, enabling the prediction of composition, processing steps, and material properties from the latent space vector. By integrating this deep learning model with a specific sampling strategy in the latent space, a novel, microstructure-centered algorithm for multiphase alloy design is developed. This algorithm is demonstrated through the design of a unified dual-phase steel, and the results are assessed at three performance levels. Moreover, an exploration into the latent vector space of the model highlights its seamless interpolation ability and its rich material information content. Notably, the current configuration of the latent space is particularly advantageous for alloy design, offering an exhaustive representation of microstructure, composition, processing, and property variations essential for multiphase alloys.

Related papers

• 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)
• DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
  We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
  arXiv  Detail & Related papers  (2024-10-17T17:20:24Z)
• Composite Material Design for Optimized Fracture Toughness Using Machine Learning [0.0]
  This paper investigates the optimization of 2D and 3D composite structures using machine learning (ML) techniques. It focuses on fracture toughness and crack propagation in the Double Cantilever Beam (DCB) test.
  arXiv  Detail & Related papers  (2024-06-23T17:01:14Z)
• IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion [97.58125811599383]
  Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge. We propose a novel Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. IME incorporates two key properties, namely space-shared property and space-specific property.
  arXiv  Detail & Related papers  (2024-03-28T23:31:25Z)
• Multi-objective Generative Design of Three-Dimensional Composite Materials [12.258353990252312]
  We report a Wasserstein generative adversarial network (MDWGAN) to implement inverse designs of 3D composite structures. Our framework is capable of tuning the properties of the generated composites in multiple aspects, while keeping the selected features of different kinds of structures.
  arXiv  Detail & Related papers  (2023-02-26T17:45:44Z)
• DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets [110.60233593474796]
  DA-VEGAN is a model with two central innovations. A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture. A custom differentiable data augmentation scheme is developed specifically for this architecture.
  arXiv  Detail & Related papers  (2023-02-17T08:49:09Z)
• RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design [65.41144149958208]
  This study aims to systematically construct a data-driven RNA design pipeline. We crafted a benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. We incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process.
  arXiv  Detail & Related papers  (2023-01-25T17:19:49Z)
• A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization [1.6311150636417262]
  In this paper, we introduce a multi-task learning approach for material texture optimization. The approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese networks.
  arXiv  Detail & Related papers  (2021-10-27T08:25:26Z)
• Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems [20.659457956055366]
  We propose a novel data-driven metamaterial design framework based on deep generative modeling. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems.
  arXiv  Detail & Related papers  (2020-06-27T03:56:55Z)
• Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process [18.17435834037483]
  We develop a multi-response latent-variable Gaussian process (LVGP) model for the microstructure libraries of metamaterials. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses. We show that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures.
  arXiv  Detail & Related papers  (2020-06-27T03:55:52Z)
• Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition [49.463864096615254]
  We analyze the influence of learning complexity, including the input complexity and model complexity. We propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data. We develop three parameter-free modules to integrate with RCN without increasing any learnable parameters.
  arXiv  Detail & Related papers  (2020-06-17T06:19:24Z)

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.