De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness–density scaling, whose structural disorder—rather than order—is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.</p

Mechanical metamaterials and beyond

Abstract Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

Funder: United States Department of Defense | United States Navy | ONR | Office of Naval Research Global (ONR Global); doi: https://doi.org/10.13039/100007297; Grant(s): N00014-20-1-2504:P00001Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

Abstract Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning.

Funder: United States Department of Defense | United States Navy | ONR | Office of Naval Research Global; Grant(s): N00014-20-1-2504:P00001Funder: United States Department of Defense | United States Navy | ONR | Office of Naval Research Global (ONR Global); Grant(s): N00014-20-1-2504:P00001Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning.

Funder: United States Department of Defense | United States Navy | ONR | Office of Naval Research Global (ONR Global); doi: https://doi.org/10.13039/100007297; Grant(s): N00014-20-1-2504:P00001Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles

Supramolecular assembly of blue and green halide perovskites with near-unity photoluminescence

The metal-halide ionic octahedron is the optoelectronic unit for halide perovskites, and a crown ether-assisted supramolecular assembly approach can pack various ionic octahedra into tunable symmetries. In this work, we demonstrate near-unity photoluminescence quantum yield (PLQY) blue and green emission with the supramolecular assembly of hafnium (Hf) and zirconium (Zr) halide octahedral clusters. (18C6@K)2HfBr6 powders showed blue emission with a near-unity PLQY (96.2%), and green emission was also achieved with (18C6@K)2ZrCl4Br2 powders at a PLQY of 82.7%. These highly emissive powders feature facile low-temperature solution-based synthesis conditions and maintain high PLQY in solution-processable semiconductor inks under ambient conditions, and they were used in thin-film displays and emissive three-dimensional-printed architectures that exhibited high spatial resolution

A general method to synthesize and sinter bulk ceramics in seconds

Ceramics are an important class of materials with widespread applications because of their high thermal, mechanical, and chemical stability. Computational predictions based on first principles methods can be a valuable tool in accelerating materials discovery to develop improved ceramics. It is essential to experimentally confirm the material properties of such predictions. However, materials screening rates are limited by the long processing times and the poor compositional control from volatile element loss in conventional ceramic sintering techniques. To overcome these limitations, we developed an ultrafast high-temperature sintering (UHS) process for the fabrication of ceramic materials by radiative heating under an inert atmosphere. We provide several examples of the UHS process to demonstrate its potential utility and applications, including advancements in solid-state electrolytes, multicomponent structures, and high-throughput materials screening

3D Printing and processing of miniaturized transducers with near-pristine piezoelectric ceramics for localized cavitation.

The performance of ultrasonic transducers is largely determined by the piezoelectric properties and geometries of their active elements. Due to the brittle nature of piezoceramics, existing processing tools for piezoelectric elements only achieve simple geometries, including flat disks, cylinders, cubes and rings. While advances in additive manufacturing give rise to free-form fabrication of piezoceramics, the resultant transducers suffer from high porosity, weak piezoelectric responses, and limited geometrical flexibility. We introduce optimized piezoceramic printing and processing strategies to produce highly responsive piezoelectric microtransducers that operate at ultrasonic frequencies. The 3D printed dense piezoelectric elements achieve high piezoelectric coefficients and complex architectures. The resulting piezoelectric charge constant, d33, and coupling factor, kt, of the 3D printed piezoceramic reach 583 pC/N and 0.57, approaching the properties of pristine ceramics. The integrated printing of transducer packaging materials and 3D printed piezoceramics with microarchitectures create opportunities for miniaturized piezoelectric ultrasound transducers capable of acoustic focusing and localized cavitation within millimeter-sized channels, leading to miniaturized ultrasonic devices that enable a wide range of biomedical applications