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In contrast with the diversity of materials found in nature, most robots are designed with some combination of aluminum, stainless steel, and 3D-printed filament. Additionally, robotic systems are typically assumed to follow basic…

Robotics · Computer Science 2025-03-04 Andrew K. Schulz , Ayah G. Ahmad , Maegan Tucker

Soft robots achieve functionality through tight coupling among geometry, material composition, and actuation. As a result, effective design optimization requires these three aspects to be considered jointly rather than in isolation. This…

Robotics · Computer Science 2026-03-09 Vittorio Candiello , Manuel Mekkattu , Mike Y. Michelis , Robert K. Katzschmann

We investigate the role of architected thin films in the interfacial failure properties of bi-layer composites. Our results show that, while graded structures can be used to prescribe failure at the interface, they do not offer significant…

Materials Science · Physics 2026-03-10 Christian Greff , Leon Pyka , Michael Zaiser , Paolo Moretti

Mechanical metamaterials are architected manmade materials that allow for unique behaviors not observed in nature, making them promising candidates for a wide range of applications. Existing metamaterials lack tunability as their properties…

Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating design has tremendous promise, general-purpose methods do…

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials and their properties. In particular, controlling the…

Statistical Mechanics · Physics 2023-07-07 Shriram Chennakesavalu , Sreekanth K. Manikandan , Frank Hu , Grant M. Rotskoff

Mechanical metamaterials are periodic lattice structures with complex unit cell architectures that can achieve extraordinary mechanical properties beyond the capability of bulk materials. A new class of metamaterials is proposed, whose…

Applied Physics · Physics 2022-07-22 Marius Wagner , Fabian Schwarz , Nick Huber , Lena Geistlich , Henning Galinski , Ralph Spolenak

Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly learn physical laws from data, we propose a Hybrid…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Elias Berger , Muhammad Usama , Jan Mehlstäubl , Bernhard Saske , Kristin Paetzold-Byhain

The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of…

Materials Science · Physics 2025-02-21 Xiao-Qi Han , Xin-De Wang , Meng-Yuan Xu , Zhen Feng , Bo-Wen Yao , Peng-Jie Guo , Ze-Feng Gao , Zhong-Yi Lu

Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques…

Applied Physics · Physics 2023-12-12 Hanxun Jin , Horacio D. Espinosa

Materials that behave like machines, e.g. functional materials that are able to change shape in response to external stimuli (Bhattacharya and James, 2005), often do so by exploiting phase transitions. Shape memory materials and the tail…

Materials Science · Physics 2019-03-13 Matthew P. O'Donnell , Jonathan P. Stacey , Isaac V. Chenchiah , Alberto Pirrera

Model-based compression is an effective, facilitating, and expanded model of neural network models with limited computing and low power. However, conventional models of compression techniques utilize crafted features [2,3,12] and explore…

Machine Learning · Statistics 2018-07-10 Hamed Hakkak

The project aims to explore a novel way to design and produce cellular materials with good energy absorption and recoverability properties. Spinodoid structures offer an alternative to engineering structures such as honeycombs and foam with…

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic…

Materials Science · Physics 2023-05-12 Joseph B. Choi , Phong C. H. Nguyen , Oishik Sen , H. S. Udaykumar , Stephen Baek

Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in…

Materials Science · Physics 2020-10-20 Xin Liu , Su Tian , Fei Tao , Haodong Du , Wenbin Yu

The design of specified nonlinear mechanical responses into a structure or material is a highly sought after capability, which would have a significant impact in areas such as wave tailoring in metamaterials, impact mitigation, soft…

Materials Science · Physics 2023-12-01 Brianna MacNider , Ian Frankel , Kai Qian , Alan Pozos , Aketzali Santos , H. Alicia Kim , Nicholas Boechler

Using a geometric formalism of elasticity theory we develop a systematic theoretical method for controlling and manipulating the mechanical response of slender solids to external loads. We formally express global mechanical properties…

Soft Condensed Matter · Physics 2021-05-04 Michal Arieli , Eran Sharon , Michael Moshe

Energy absorbing materials, like foams used in protective equipment, are able to undergo large deformations under low stresses, reducing the incoming stress wave below an injury or damage threshold. They are typically effective in absorbing…

Soft Condensed Matter · Physics 2019-09-12 Yifan Wang , Brian Ramirez , Kalind Carpenter , Christina Naify , Douglas C. Hofmann , Chiara Daraio

Knitting turns yarn, a 1D material, into a 2D fabric that is flexible, durable [1], and can be patterned to adopt a wide range of 3D geometries [2]. Like other mechanical metamaterials [3], the elasticity of knitted fabrics is an emergent…

The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale model training and evaluation. We set out to simplify this…