Metamaterials are engineered composites that exhibit properties not found in naturally occurring materials. They allow scientists to design materials with customized properties such as negative refractive indexes, tunable conductivity, and nonlinearity. Metamaterials hold great promise for revolutionary applications in optics, electronics, acoustics, robotics, and more. Artificial intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, and decision-making. Powered by algorithms and neural networks that learn from data, AI systems are achieving human-level capabilities in a growing number of domains.
Though arising from disconnected fields, metamaterials and AI are prime candidates for convergence. The design of metamaterials involves navigating vast compositional parameter spaces to identify combinations that produce target properties. This multifaceted optimization process demands computational approaches. Herein lies the potential of AI — equipped with learning and self-improvement capabilities, AI algorithms could accelerate metamaterial development. This includes designing metamaterial blueprints and structures, predicting their characteristics, and autonomously conducting virtual or real-world experiments to validate designs. Expert systems could capture knowledge from metamaterial databases and scientific literature into easily query able formats. By integrating AI into all aspects of inquiry, the pace of metamaterial innovation could drastically quicken. The possibilities span custom materials for next-generation devices, dynamic materials with self-adaptation abilities, and metamaterial discovery becoming an automated, data-driven science. The convergence of metamaterials and AI heralds an era of accelerated, democratized metamaterials research that could unleash novel, world-changing applications.
Metamaterials are engineered composites that derive their properties not just from their constituent materials but also from their precisely designed artificial structures. This enables metamaterials to exhibit behavioral properties that surpass what is observed in nature — a feat that gives them the “meta” distinction. Their precisely tailored structures allow scientists to imbue counterintuitive properties like negative refractive indexes, low/zero densities, and nonlinear behaviors.
In contrast with natural materials whose optoelectronic, acoustic, and mechanical properties are fixed, metamaterials offer customizability and multifunctionality. Their capabilities arise from structural effects rather than just chemical composition. This structure-driven versatility makes them attractive for diverse cutting-edge applications.
The foundations of metamaterials research began in the late 1960s when scientists explored artificial dielectrics made from lattices of metal spheres and rods. The concept that material properties could be influenced by sub-wavelength-scale resonant structures led to several pivotal breakthroughs. In 1999, physicist John Pendry theorized that composite metamaterials could be designed to have a negative index of refraction. This was soon experimentally demonstrated by David Smith’s team in 2000 — the first metamaterial exhibiting an optical property absent in natural materials.
In 2006, engineers from Duke University created the first acoustic cloak able to bend sound waves around an object, rendering it invisible to sound. Not long after, invisibility cloaks that could hide macroscale objects from microwaves, infrared waves, and visible wavelengths followed. Demonstrating that objects could be cloaked across much of the electromagnetic spectrum sparked great interest in metamaterials. The 2000s also saw pioneers like Xiang Zhang advancing metamaterials that could manipulate light with unprecedented precision — enabling applications like superlenses with sub-wavelength resolution.
Present-day metamaterials are bringing such exotic functionality into real-world operation. In 5G networks, metamaterials enable frequency-agnostic and ultra-compact antennas. Health imaging systems use metamaterials to go beyond typical resolution limits during cancer screening and diagnosis. Acoustic metamaterials show promise in noise control — for soundproofing structures and designing less noisy jet engines. Meta-surfaces fitted onto standard lenses can create single-shot 3D images, miniaturize bulky lens systems in phones and glasses, and correct for optical aberrations in microscopes used in biology labs. The list goes on from Hall thrusters in satellites, chemistry labs-on-a-chip, to smart textiles and sensors for Internet of Things ecosystems.
As scientists uncover more metamaterial compositions and nanoscale topologies, the feature sets of these unnatural materials will continue expanding. This ever-growing design space is also where artificial intelligence can catalyze metamaterials development.
Artificial intelligence (AI) broadly refers to computer systems that can perform tasks requiring human-level intelligence including learning, problem-solving, speech and image recognition, and decision making. AI owes its growing capabilities to advances in algorithms, availability of big data, and increased computing power — especially that of graphics processing units (GPUs) which excel at the mathematical operations underpinning most AI.
AI, especially its subsets of machine learning (ML) and deep learning (DL), is profoundly impacting scientific research. From managing enormous volumes of output data to automating the most tedious tasks, AI is boosting R&D productivity across industries. This also holds true for material science — an increasingly data-rich and computationally demanding field aiming to uncover new substances with valuable properties.
Modern material discovery involves navigating complex landscapes of molecular and structural compositions to determine optimal material formulations. The process entails repeatedly predicting molecular behaviors, synthesizing the best candidates, and characterizing the results. AI accelerates multiple steps within this pipeline. Sophisticated ML models can predict properties of hypothetical materials to recommendation promising formulations. Some models can generate molecular graphs and crystal structures with desired features instead of merely screening existing databases.
AI also assists in material characterization by rapidly analyzing complex data from techniques like high-throughput microscopy, X-ray diffraction, and mass spectrometry. Deep learning aids in translating raw characterization data into actionable insights about material microstructures and performances. Further, AI optimizes manufacturing coordination and quality control — ensuring consistency across material production cycles.
Several case studies highlight AI’s utility. Researchers employed Bayesian optimization algorithms to discover polymer films with highly customized optical transmission spectra in just ten automated experiments. Machine learning guided the fabrication of perovskite solar cell films with record conversion efficiencies. Autonomous research platforms, like Zymergen’s robot-operated labs, rapidly evolve enzymes and microbes critical for biomanufacturing.
AI-designed power semiconductors, metallic glasses with unmatched hardness, and organic photovoltaic formulations demonstrate AI’s enormous potential to expand the materials innovation landscape. With smarter search algorithms and increasing data generation, AI systems can exponentially quicken the pace of high-performance material development to meet pressing demands.
Looking ahead, AI incorporation into all facets of inquiry could transform material science into a data-driven, self-improving discipline. Intelligent systems compensating for and augmenting human capabilities will pave the way for customizable, multipurpose, and previously unfathomable materials.
Metamaterials and artificial intelligence (AI) are two of the most disruptive technologies today. Though seemingly disconnected fields, their convergence promises to profoundly reshape metamaterials research and applications.
As engineered composites exhibit extraordinary properties, designing metamaterials involves navigating immense material and structural parameter spaces. Scientists must meticulously model combinations of shape, size, orientation, and arrangement of substructures like rods, rings, and helices embedded in host matrices ranging from metals to dielectrics to tune desired responses. Slight changes in these topological motifs lead to vastly different optical, acoustic, or mechanical phenomena.
Presently, metamaterial development relies heavily on human intuition during prototyping cycles. However, as libraries of metamaterial architectures grow more complex, AI systems hold the key to efficiently search, characterize, and refine these immense design spaces. Powered by advances in artificial neural networks, multi-objective optimizations, and evolutionary algorithms, AI can revolutionize metamaterial discovery — automating the most challenging design tasks.
For instance, machine learning models can probe physics simulations and experimental data to identify topological motifs and compositions that satisfy multiple property targets. Trained on a dataset spanning many metamaterial families, metal fractions, and functionalities, deep neural networks can suggest completely new combinations and structures to synthesize. Such AI exploration of gigantic design universes can uncover exotic metamaterials difficult for humans to conceptualize independently.
Looking ahead, by integrating AI into all aspects of metamaterial modeling, fabrication, and characterization, rapid property mapping across vast libraries of unit cell geometries could enable on-demand design of application-specific metamaterials. Further, real-time model predictive control systems could handle microstructural tuning and reconfiguration in response to dynamic requirements and fluctuating environments. Such embedded AI could actualize autonomous metamaterials with self-optimization abilities.
Startups at this exciting intersection include Anthropic and MultiWave AI. Leveraging natural language processing and physical simulation, Anthropic aims to generate molecular structures with desired properties purely in-silico with no real-world experiments. Though still in the initial stages, their AI assistants promise to accelerate metamaterials development. Similarly, MultiWave employs AI to design electromagnetic and acoustic metamaterials for defense and communication sectors. Through virtual testing on an expandable dataset, their algorithms rapidly converge on device-ready metamaterial prototypes.
On the academic front, researchers from RMIT University developed an AI technique that learned design rules from a metadataset of 2D metamaterial patterns and corresponding optical properties. Their deep convolutional neural network then generated design blueprints for high-performance metasurfaces. At Rice University, machine learning guides rapid 3D printing of dielectric resonator metamaterials with spectrally selective absorption tailored for infrared spectroscopy and thermal cameras. These projects exemplify the advantages of merging human expertise with AI during metamaterial synthesis and characterization.
As tools, data, and computing infrastructures continue advancing, AI-driven metamaterials research will become widespread. Intelligent algorithms mitigating human limitations may finally unleash metamaterials’ full disruptive potential across industries from medicine to aerospace. Further co-development of these exponentially growing technologies promises an exciting future where creating materials with bespoke responses becomes a routine exercise.
The amalgamation of artificial intelligence and metamaterials is set to transform a wide range of technologies. As AI algorithms get better at navigating gigantic metamaterial design spaces, various industrial, medical, and consumer applications will come within reach.
One immense opportunity is developing dynamic metamaterials that reconfigure themselves in response to the environment. Already AI empowers limited autonomous functionalities in metasurfaces. As algorithms and sensing improve, real-time reconfigurable metamaterials could enable extremely responsive devices. For example, meta-optic components could continuously tune properties like light polarization, orbital angular momentum, chromatic dispersion, and focal distance to actively cancel image aberrations in microscopes. Such adaptive optics will generate crisp, ultra-high-resolution imagery to accelerate biological research.
Expanding compute and material fabrication capabilities may someday allow smartphones to replace multiple cameras and lenses with a single AI-controlled metasurface camera. With no moving parts, its intelligent pixel architecture could manipulate impinging light to switch between telephoto, wide-angle, macro, and high-speed modes via software controls alone. Developing such multifunctional meta-optics requires AI capabilities.
AI empowers superior sensing by designing metamaterials that surpass natural material properties. Meta-sensors with million-fold enhanced sensitivities could enable rapid disease screening by detecting minute biomarkers. Combined with microfluidics, such lab-on-a-chip diagnostics may one day perform complex assays autonomously within minutes.
Ultra-efficient meta-photovoltaics will aid sustainable energy production by optimizing light absorption and charge separation. Meanwhile, metamaterial-enhanced wireless charging solutions could revolutionize consumer electronics by distributing ambient electromagnetic energy to power Internet of Things ecosystems. Additionally, safer and higher capacity meta-batteries designed by AI could accelerate transportation electrification.
The applications expand further when metamaterials meet robotics. Metamaterial skins enabling unparalleled tactile sensing and adaptive camouflage could give rise to markedly smarter robots. Parallel developments in metafluidics may realize highly dexterous actuators for more agile robots. Further, incorporating metastructure movement mechanics could enable shapeshifting robot’s adept at navigating dynamic real-world environments. AI will play an indispensable role in coordinating these multifunctionalities to direct autonomous adaptation.
On the biomedical front, AI simplification of medical meta-device fabrication using 3D printing or self-assembly approaches could enable patient-specific treatments with noninvasive surgeries. Continued convergence could transform metamaterials into intelligent biomaterials providing regenerative scaffolding or programmable drug delivery.
However, as with most exponentially growing technologies, AI-designed metamaterials also raise complex ethical questions regarding equitable access to the tools, data, and products as well as misuse risks — aspects that must run parallel to any advances.
Overall, the fusion of artificial intelligence and engineered materials constitutes the next frontier that stretches the bounds of human imagination. AI-discovered metamaterials promise to solve long-standing global issues — if developed responsibly and for collective benefit.
While combining artificial intelligence and metamaterials promises widespread innovations, several imminent challenges require solutions. Key issues center on computational scaling, quality datasets, and responsible development frameworks.
Designing and optimizing metamaterials demands processing complex physics models and multidimensional data. As metamaterials grow more multifunctional, intricate simulations strain even high-performance computing. Evolving novel metamaterial blueprints requires evaluating billions of topological permutations — a formidable task even for optimized AI models. Advances in AI chips specialized for tensor operations combined with cloud and quantum computing could alleviate this limitation.
Further, training reliable AI prediction models relies on quality experimental data. However, metamaterials research typically focuses on demonstrating proof-of-concepts, not generating reproducible benchmarks. The lack of standardized testing and property documentation makes compiling training datasets difficult — a key bottleneck holding back AI incorporation. Material science overall grapples with this issue. Initiatives like the Materials Data Facility offer templates to uniformly capture, log, and share materials data to power AI growth.
As with any exponentially growing technology, ethical challenges around access, privacy, security, and good faith also emerge. Open issues include democratizing metamaterial design tools, handling sensitive customer data safely, monitoring for toxicity or durability issues in new materials, and preventing dual-use of certain scattering control capabilities. Policy frameworks addressing these concerns must evolve proactively, not reactively.
Beyond scientific obstacles, this convergence opens several opportunities. Integrating metamaterials and AI into physics and engineering curriculums can groom superior hybrid talents for material innovation roles. Sustained progress will hinge on interdisciplinary expertise able to traverse artificial intelligence, physics simulations, microfabrication techniques, and laboratory skills seamlessly.
Economically, discernible market needs from electronics to medicine indicate high commercialization potential for AI-discovered metamaterials. Entrepreneurial activity already blossoms around custom meta-optics for augmented reality and super-resolution imaging. Metamaterials startups could drive job creation through processing, testing, scale-up, and deployment.
This synergistic combination placing machine learning into human hands could propel metamaterials into addressing global priorities like clean energy, affordable healthcare, and environmental rejuvenation. With care and collective effort, AI and metamaterials may fulfill their most meaningful purpose — uplifting society.
The message for stakeholders is clear — prioritize long-term cooperation on open datasets and tools, platform governance, commercial maturation pathways, and democratization measures. With vision and responsibility, this merger of exponentially growing technologies could profoundly elevate human potential.
This article explored the convergence of artificial intelligence and engineered metamaterials — one of science’s most promising opportunities to accelerate discovery. We surveyed metamaterials’ expanding applications based on their customizable optical, acoustic, and electromagnetic properties. Contrasting them with natural materials revealed immense yet challenging design spaces requiring computational approaches.
Introducing AI and its rising significance across material research provided context before detailing key uses in predicting properties, generating molecules, and optimizing production. Pivotal case studies showcased AI accelerating innovations from drug encapsulants to 5G antennas. We then dived into AI’s burgeoning role in unlocking metamaterials’ full potential by rapidly navigating their massive compositional and configurational possibilities. Ongoing efforts exemplified machine learning modeling optical megastructures, guiding 3D printing routines, and inspiring completely novel topologies.
Exciting prospective applications suggest AI and metamaterials could soon catalyze advancements across sectors from resilient infrastructure to biomedical devices. However, realizing this requires solving challenges in computing power, tools access, ethics, and dataset limitations while harnessing opportunities like educational transformation and job creation. International initiatives prioritizing open collaboration on AI-powered metamaterial research can set the stage for solutions meeting global needs.
As this fusion continues unfolding, scientists are poised to propel an existing radical innovation into revolutionary territories promising superior human futures. With responsible development, this interdisciplinary merger represents technology and science transcending perceived constraints to manifest audacious new realities aligned to humanitarian ideals. Our shared progress depends on pooling knowledge across specialties to serve that unified vision of empowerment.