Materials Design Through Batch Bayesian Optimization with Multisource Information Fusion

JOM ◽  
2020 ◽  
Vol 72 (12) ◽  
pp. 4431-4443
Author(s):  
Richard Couperthwaite ◽  
Abhilash Molkeri ◽  
Danial Khatamsaz ◽  
Ankit Srivastava ◽  
Douglas Allaire ◽  
...  
Keyword(s):  
Information Fusion ◽  
Materials Design ◽  
Bayesian Optimization

A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems

2018 ◽  
Author(s):  
Xiaolin Li ◽  
Zijiang Yang ◽  
L. Catherine Brinson ◽  
Alok Choudhary ◽  
Ankit Agrawal ◽  
...  
Keyword(s):  
Latent Variables ◽  
Materials Design ◽  
Information Loss ◽  
Bayesian Optimization ◽  
Generative Adversarial Networks ◽  
Microstructural Characteristics ◽  
Significant Information ◽  
Adversarial Learning ◽  
Design Variables ◽  
Microstructural Design

In Computational Materials Design (CMD), it is well recognized that identifying key microstructure characteristics is crucial for determining material design variables. However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

scholarly journals Microstructural Materials Design Via Deep Adversarial Learning Methodology

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Zijiang Yang ◽  
Xiaolin Li ◽  
L. Catherine Brinson ◽  
Alok N. Choudhary ◽  
Wei Chen ◽  
...  
Keyword(s):  
Latent Variables ◽  
Network Architecture ◽  
Materials Design ◽  
Information Loss ◽  
Bayesian Optimization ◽  
Generative Adversarial Networks ◽  
Microstructural Characteristics ◽  
Significant Information ◽  
Adversarial Learning ◽  
Design Variables

Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

scholarly journals Data-Centric Mixed-Variable Bayesian Optimization for Materials Design

2019 ◽  
Author(s):  
Akshay Iyer ◽  
Yichi Zhang ◽  
Aditya Prasad ◽  
Siyu Tao ◽  
Yixing Wang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Latent Variable ◽  
Pareto Frontier ◽  
Material Design ◽  
Materials Design ◽  
Bayesian Optimization ◽  
Data Uncertainty ◽  
Expected Improvement ◽  
Nonlinear Design ◽  
Mixed Variable

Abstract Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify “lack of data” uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.

Thermodynamics for materials design

1993 ◽  
Vol 90 ◽  
pp. 275-280 ◽  
Author(s):  
B Sundman
Keyword(s):  
Materials Design
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