Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
- URL: http://arxiv.org/abs/2401.13570v2
- Date: Mon, 10 Jun 2024 11:26:14 GMT
- Title: Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
- Authors: Yanyan Yang, Lili Wang, Xiaoya Zhai, Kai Chen, Wenming Wu, Yunkai Zhao, Ligang Liu, Xiao-Ming Fu,
- Abstract summary: This paper proposes a fast inverse design method, whose core is an advanced deep generative AI algorithm, to generate voxel-based mechanical metamaterials.
Specifically, we use the self-conditioned diffusion model, capable of generating a microstructure with a resolution of $1283$ to approach the specified homogenized matrix in just 3 seconds.
- Score: 41.97258566607252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mechanical metamaterial is a synthetic material that can possess extraordinary physical characteristics, such as abnormal elasticity, stiffness, and stability, by carefully designing its internal structure. To make metamaterials contain delicate local structures with unique mechanical properties, it is a potential method to represent them through high-resolution voxels. However, it brings a substantial computational burden. To this end, this paper proposes a fast inverse design method, whose core is an advanced deep generative AI algorithm, to generate voxel-based mechanical metamaterials. Specifically, we use the self-conditioned diffusion model, capable of generating a microstructure with a resolution of $128^3$ to approach the specified homogenized tensor matrix in just 3 seconds. Accordingly, this rapid reverse design tool facilitates the exploration of extreme metamaterials, the sequence interpolation in metamaterials, and the generation of diverse microstructures for multi-scale design. This flexible and adaptive generative tool is of great value in structural engineering or other mechanical systems and can stimulate more subsequent research.
Related papers
- Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials [0.0]
This paper defines several design spaces for chiral metamaterials, including the ligament contact angles, the ligament shape, and circle radius.
We then leverage machine learning approaches, and specifically Bayesian optimization, to determine optimally performing designs satisfying maximal non-reciprocity or stiffness asymmetry.
Our analysis of the underlying mechanisms reveals that chiral metamaterials that can display multiple different contact states under loading in different directions are able to simultaneously exhibit both high non-reciprocity and stiffness asymmetry.
arXiv Detail & Related papers (2024-04-19T23:39:56Z) - Mechanical Characterization and Inverse Design of Stochastic Architected
Metamaterials Using Neural Operators [2.4918888803900727]
Machine learning is emerging as a transformative tool for the design of architected materials.
Here, we introduce a new end-to-end scientific ML framework, leveraging deep neural operators (DeepONet)
Results obtained from spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%.
arXiv Detail & Related papers (2023-11-23T05:23:15Z) - 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) - Linking Properties to Microstructure in Liquid Metal Embedded Elastomers
via Machine Learning [0.0]
Liquid metals (LM) are embedded in an elastomer matrix to obtain soft composites with unique thermal, dielectric, and mechanical properties.
By linking the structure to the properties of these materials, it is possible to perform material design rationally.
arXiv Detail & Related papers (2022-07-24T06:02:26Z) - Three-dimensional microstructure generation using generative adversarial
neural networks in the context of continuum micromechanics [77.34726150561087]
This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation.
The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
arXiv Detail & Related papers (2022-05-31T13:26:51Z) - Learning Geometrically Disentangled Representations of Protein Folding
Simulations [72.03095377508856]
This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein.
Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules.
Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
arXiv Detail & Related papers (2022-05-20T19:38:00Z) - 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) - A deep learning driven pseudospectral PCE based FFT homogenization
algorithm for complex microstructures [68.8204255655161]
It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
arXiv Detail & Related papers (2021-10-26T07:02:14Z) - 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)
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.