The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have drastically reduced the costs traditionally associated with high-resolution simulations, allowing for more detailed and complex modeling of materials across various scales. Despite these advancements, the fine-tuning of data-driven models remains costly, particularly for large setups. Moreover, as these models increasingly capture intricate relationships between processes, microstructure, and material properties, it is crucial to ensure predictions are physically interpretable to translate data into actionable insights.
This Research Topic aims to showcase the latest advancements and state-of-the-art methods in applying deep learning to materials science and mechanics. It focuses on both theoretical and practical applications to propel the field forward. By highlighting new algorithms and modeling techniques, this topic seeks to tackle existing challenges in computational materials science, including the need for more efficient and interpretable models that can explore vast parameter spaces for material design and optimization while adhering to physical laws.
To gather further insights in this rapidly advancing field, we welcome articles addressing, but not limited to, the following themes:
- Parametric learning of governing equations, with methods to handle complex geometries and sparsity
- Physics-informed and physics-encoded machine learning advancements
- Deep learning for constitutive material modeling to predict and explore nonlinear dependencies
- Uncertainty quantification in material predictions for robust design application
- Inverse design using extensive datasets for optimized material configurations
- Generative models and autonomous discovery capabilities in material science, harnessing techniques like variational autoencoders and GANs for new materials and structures
The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have drastically reduced the costs traditionally associated with high-resolution simulations, allowing for more detailed and complex modeling of materials across various scales. Despite these advancements, the fine-tuning of data-driven models remains costly, particularly for large setups. Moreover, as these models increasingly capture intricate relationships between processes, microstructure, and material properties, it is crucial to ensure predictions are physically interpretable to translate data into actionable insights.
This Research Topic aims to showcase the latest advancements and state-of-the-art methods in applying deep learning to materials science and mechanics. It focuses on both theoretical and practical applications to propel the field forward. By highlighting new algorithms and modeling techniques, this topic seeks to tackle existing challenges in computational materials science, including the need for more efficient and interpretable models that can explore vast parameter spaces for material design and optimization while adhering to physical laws.
To gather further insights in this rapidly advancing field, we welcome articles addressing, but not limited to, the following themes:
- Parametric learning of governing equations, with methods to handle complex geometries and sparsity
- Physics-informed and physics-encoded machine learning advancements
- Deep learning for constitutive material modeling to predict and explore nonlinear dependencies
- Uncertainty quantification in material predictions for robust design application
- Inverse design using extensive datasets for optimized material configurations
- Generative models and autonomous discovery capabilities in material science, harnessing techniques like variational autoencoders and GANs for new materials and structures