Inverse-designed spinodoid metamaterials
By Siddhant Kumar, Stephanie Tan, Li Zheng, Dennis M. Kochmann. npj Computational Materials, 2020
393 citations recorded by the source corpus.
After a decade of periodic truss-, plate-, and shell-based architectures having dominated the design of metamaterials, we introduce the non-periodic class of spinodoid topologies. Inspired by natural self-assembly processes, spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation. Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space. Counter-intuitively, breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading. We introduce an efficient and robust machine learning technique for the inverse design of (meta-)materials which, when applied to spinodoid topologies, enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent (anisotropic) stiffness and density. We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.
Subfields: Civil and Structural Engineering, Materials Chemistry, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/cbe038d27b211e109c9ea382f8ff74639d5d7c19
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Citations in the corpus
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “There is also increasing interest in the inverse design of materials using ML, where desired target properties of materials are used as input to the model to identify atomic or microscale structures that exhibit such properties [156, 172, 222, 243].”
- Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “There is also increasing interest in the inverse design of materials using ML, where desired target properties of materials are used as input to the model to identify atomic structures that exhibit such properties [137, 151, 202, 226].”