Mesoscopic and multiscale modelling in materials
The concept of multiscale modelling has emerged over the last few decades to describe
procedures that seek to simulate continuum-scale behaviour using information gleaned from …
procedures that seek to simulate continuum-scale behaviour using information gleaned from …
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques
Building sensible processing-structure-property (PSP) links to gain fundamental insights and
understanding of materials behavior has been the focus of many works in computational …
understanding of materials behavior has been the focus of many works in computational …
[HTML][HTML] A transfer learning approach for microstructure reconstruction and structure-property predictions
Stochastic microstructure reconstruction has become an indispensable part of computational
materials science, but ongoing developments are specific to particular material systems. In …
materials science, but ongoing developments are specific to particular material systems. In …
Deep materials informatics: Applications of deep learning in materials science
A Agrawal, A Choudhary - Mrs Communications, 2019 - cambridge.org
The growing application of data-driven analytics in materials science has led to the rise of
materials informatics. Within the arena of data analytics, deep learning has emerged as a …
materials informatics. Within the arena of data analytics, deep learning has emerged as a …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
[HTML][HTML] Machine-learning-assisted de novo design of organic molecules and polymers: opportunities and challenges
Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …
chemical, and materials science fields. Traditional design approaches for organic molecules …
Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative …
Direct prediction of material properties from microstructures through statistical models has
shown to be a potential approach to accelerating computational material design with large …
shown to be a potential approach to accelerating computational material design with large …
Stochastic microstructure characterization and reconstruction via supervised learning
Microstructure characterization and reconstruction have become indispensable parts of
computational materials science. The main contribution of this paper is to introduce a …
computational materials science. The main contribution of this paper is to introduce a …
Microstructure representation and reconstruction of heterogeneous materials via deep belief network for computational material design
Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of
complex material systems by integrating material science and design automation. For …
complex material systems by integrating material science and design automation. For …
Microstructure reconstruction using diffusion-based generative models
This paper proposes a microstructure reconstruction framework with denoising diffusion
models for the first time. The novelty and strength of the proposed model lie in its universality …
models for the first time. The novelty and strength of the proposed model lie in its universality …