Differentiable graph-structured models for inverse design of lattice materials

Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspiration toward their design is in the irregular micro-architectures of nature. However, the immense design variability unlocked by such irregularity is challenging to probe analytically. Here, we propose a new computational approach using graph-based representation for regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, therefore allowing automatic differentiation with surrogate derivatives to adjust both geometric structure and local attributes of individual lattice elements to achieve inversely designed materials with desired properties. We further introduce a graph neural network surrogate model for structural analysis at scale. The methodology is generalizable to any system representable as heterogeneous graphs.Comment: Code: https://gitlab.com/EuropeanSpaceAgency/pylattice2

2020 Proceedings of the 3rd International Conference on Trauma Surgery Technology in Giessen

The 3 rd event of the Giessen International Conference on Trauma Surgery Technology on October, the 17th 2020 was hosted on Zoom in accordance with the worldwide corona situation. Dr Mieczakowski, Dr Yu, and Wolfram drafted in 2018 from Jan’s apartment in Bremen the manuscript which was submitted to and approved for funding by the Deutsche Forschungsgemeinschaft (DFG). At that time, we had no idea what substantial changes the conferencing concept would require. This is why we would like to thank again Michele. She first planned this year’s event after the 2019 date and then in the spring of 2020 had to replan for the new situation

Designing for Disorder: The Mechanical Behaviour of Bioinspired, Stochastic Honeycomb Materials

In nature, structure, material and function are constantly evolving in tandem. This work employs polymer 3D printing to study new honeycomb materials inspired by disordered, hierarchical architectures in biomineralized organisms. The primary aim is to elucidate mechanical effects of structural order vs. disorder in natural cellular solids. New honeycomb materials are proposed with improved damage tolerance. A mathematical “regularity parameter” controls cell stochasticity. Uniaxial tension, compression and fracture experiments reveal significant crack path deviations and strain delocalization. These lead to enhancements in e.g. ductility and fracture toughness between 30-90% beyond periodic geometries. Optimal cell irregularities are suggested, revealing a relationship between damage tolerance and cell size. Conserving spatial density, hexagonal honeycombs composed of hierarchical micro-truss ligaments are also presented. Depending on design objective, 100% increase in compressive strength and three-fold energy absorption limits were achieved. These results comprise novel design spaces, where disorder and hierarchy are embraced as design variables.M.A.S

Neural Inverse Design of Nanostructures [Dataset]

About The Project Built With Getting Started Prerequisites Installation Test Usage Contributing License ContactNeural Inverse Design of Nanostructures (NIDN) is a Python project by the Advanced Concepts Team of ESA. The goal of the project is to enable inverse design of stacks of nanostructures, metamaterials, photonic crystals, etc., using neural networks in PyTorch. As forward models, it supports rigorous coupled-wave analysis and a finite-difference time-domain solver. There is an accompanying paper about to be published.Peer reviewe

Neural Inverse Design of Nanostructures (NIDN)

In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristics. NIDN supports two different solvers, rigorous coupled-wave analysis and a finite-difference time-domain method. The utility and validity of NIDN are demonstrated on several synthetic examples as well as the design of a 1550 nm filter and anti-reflection coating. Results match experimental baselines, other simulation tools, and the desired spectral characteristics. Given its full modularity in regard to network architectures and Maxwell solvers as well as open-source, permissive availability, NIDN will be able to support computational material design processes in a broad range of applications