AI Meets Nature: Massive Study Maps Biomimetic Research and Exposes a Surprising Energy Gap

Nature has spent billions of years perfecting its designs, and engineers have spent decades trying to copy them. Now, a sweeping bibliometric analysis published in Heliyon has, for the first time, mapped the entire research landscape where artificial intelligence and biomimetics intersect, revealing a field that is booming in robotics and computing but strikingly silent on one of humanity’s most urgent challenges: energy. The study, led by Nadiya Mehraj and colleagues including Luisa F. Cabeza, a renowned expert in thermal energy storage, analysed 9,872 documents published between January 1990 and December 2024, drawn from the Scopus database across 109 countries and regions. The results paint a vivid picture of an interdisciplinary revolution in full swing, while simultaneously exposing a knowledge gap that could shape the next decade of sustainable technology research.

The sheer scale of the growth is remarkable. Publications in AI-driven biomimetics remained sparse through the 1990s, when only a handful of pioneering works laid the conceptual foundations. Interest began accelerating after 2000, but the real explosion came after 2016, driven by advances in machine learning, optimisation algorithms, and bio-inspired computing. The field peaked in 2021 with a surge of highly cited studies, and although publication volumes stabilised between 2022 and 2024, the authors interpret this as a sign of maturity rather than decline, with research efforts consolidating and new thematic areas emerging. Citation patterns showed peaks around 2015 and 2020, indicating that certain studies gained considerable academic influence and that researchers are increasingly building cumulatively on previous findings rather than working in isolation.

To construct this map, the team employed VOSviewer version 1.6.20, a widely used tool for constructing and visualising bibliometric networks. They combined citation analysis with content analysis, examining publication trends, co-authorship networks, and keyword co-occurrence patterns. The search query used Boolean operators to pair AI-related terms, including artificial intelligence, machine learning, neural networks, model predictive control, and evolutionary algorithms, with biomimetics-related stems such as biomimetic, biomimicry, bioinspired, and bio-inspired. Author keywords were cleaned using a thesaurus file to merge spelling variants and abbreviations, and only keywords appearing at least six times were included in the final co-occurrence network. Out of 17,552 unique keywords, 103 met this threshold, providing a statistically robust picture of the field’s conceptual structure.

The keyword analysis revealed a field organised around three dominant clusters. The first and most prominent centres on AI in biomimetic robotics, where terms such as bio-inspired robotics, artificial neural networks, reinforcement learning, and swarm intelligence form dense, strongly connected associations. The second cluster revolves around optimisation and computational intelligence, featuring evolutionary algorithms, genetic algorithms, particle swarm optimisation, and deep learning. The third, spanning neuromorphic computing, synaptic learning, and memristive devices, bridges AI, neuroscience, and biomimicry, reflecting the rise of brain-inspired computing models. Artificial neural networks emerged as the single most connected keyword, with 914 occurrences and a total link strength of 954, followed by machine learning with 711 occurrences and a link strength of 843. Deep learning showed a notably recent average publication year of 2021.47, confirming its rapid ascent within the field.

Then comes the surprise. Energy-related terms barely register on the map. The keyword energy ranked just 41st, with only 49 occurrences and a total link strength of 82, while maximum power point tracking, a cornerstone of renewable energy systems, ranked 98th with a mere 14 occurrences. In the citation analysis by research field, Energy accounted for just 119 citations, compared with 2,687 for Engineering and 1,602 for Science. The overlay visualisation, in which colours represent average publication years, confirmed that energy terms sit at the periphery of the network, weakly linked to the central AI and optimisation clusters. For a field that promises sustainable, nature-inspired solutions, the near-absence of energy applications represents a striking disconnect between potential and practice.

The geographic distribution of research output adds another layer to the story. China leads with 2,136 publications, followed by India with 1,443 and the United States with 1,402. Yet when it comes to citation impact, the United States dominates with 57,016 total citations, far ahead of China’s 47,945, suggesting differences in the influence and visibility of research contributions. European nations, including the United Kingdom, Spain, Italy, Germany, and France, feature prominently, reflecting robust academic infrastructure supporting biomimetic research. Emerging economies such as Brazil, Iran, and Saudi Arabia are steadily increasing their contributions, and the collaboration network analysis revealed strong interconnections between major hubs, with newer players establishing growing ties to established research centres. On the individual level, Simon X. Yang of the University of Guelph leads the field with 38 relevant publications and an h-index of 56, while researchers from Mexico’s Tijuana Institute of Technology and Singapore’s National University also rank among the most prolific contributors.

The study also examined where this research is published and who funds the institutional engines behind it. Journal papers dominate the output, followed by conference papers, while book-length treatments remain rare, which the authors identify as a gap in long-form synthesis. Advances in Intelligent Systems and Computing leads by publication volume with 276 papers, but Advanced Materials tops citation impact with 7,888 citations, followed by IEEE Transactions on Systems, Man, and Cybernetics with 7,201. Specialised venues such as Bioinspiration and Biomimetics and Neurocomputing play a crucial bridging role between computational intelligence and bio-inspired engineering. Institutionally, Cairo University’s Faculty of Computers and Information ranks first by citations with 1,735 from just 12 documents, while the University of Chinese Academy of Sciences is the most productive with 26 publications, underscoring that computational intelligence and optimisation groups, rather than energy departments, currently drive the field.

Beneath the statistics lies a compelling technical narrative about what AI actually does for biomimetic design. Machine learning models trained on biological data can predict material properties and suggest optimal compositions, as demonstrated by AI-driven simulations of hierarchical structures found in nacre and spider silk that have informed ultra-lightweight, high-strength composites. Machine-learning-assisted design of nacre-inspired composites and gradient architectures has improved strength-toughness trade-offs, while AI-powered optimisation has refined bio-inspired surfaces such as lotus-leaf-inspired hydrophobic coatings and gecko-inspired adhesives. In robotics, neural networks, reinforcement learning, and computer vision enable bio-inspired machines to learn from their environments, adjust their movements dynamically, and navigate unpredictable terrain, with swarm intelligence coordinating collaborative robotic systems. In medicine, neural networks trained on biological datasets advance diagnostics, prosthetic design, and bio-fabrication techniques such as 3D bioprinting of tissues and organs.

So why does energy lag so far behind? The authors identify concrete technical shortfalls across three subareas. In thermal energy storage with phase change materials, AI has been used for performance prediction and parameter inference, but end-to-end, bio-inspired AI pipelines for tank geometry design, PCM selection, encapsulation, and degradation-aware control remain scarce, with few studies translating natural thermoregulation strategies into AI control policies at system scale. In solar energy, bio-inspired micro and nanostructures for light trapping and anti-reflection exist, but demonstrations at module scale incorporating durability, manufacturability, and cost in multi-objective optimisation are limited. In wind energy, AI-guided morphing surfaces modelled on bird and whale fin analogues exist primarily as prototypes or simulations, with few field-validated, AI-in-the-loop controllers that co-optimise efficiency, loads, and noise under real turbulence spectra. The pattern is consistent: promising concepts, but few validated, scalable deployments.

The message for the research community is clear. The convergence of AI and biomimetics has already transformed robotics, neuromorphic computing, and optimisation, but its application to energy systems, biomimetic materials, sensory perception mimicking echolocation or insect vision, and adaptive control remains an emerging frontier. The authors argue that closing this gap will require deliberate cross-disciplinary collaboration between AI scientists, energy engineers, and biomimetic researchers, spanning thermal energy storage, solar and wind technologies, energy harvesting, and smart grid management. Given that bio-inspired algorithms can optimise energy consumption and that AI-driven predictive models can enhance the adaptability of renewable energy systems, the untapped potential is substantial. If the trajectory of the past decade is any guide, the energy sector may be the next arena where nature’s blueprint, decoded by machines, delivers its most consequential innovations yet.

Subject of Research: Bibliometric mapping of artificial intelligence applications in biomimetic and bio-inspired technologies

Article Title: Bibliometrics in artificial intelligence in biomimetic and bio-inspired technologies: mapping the research landscape and identifying knowledge gaps

Article References: Mehraj, N., Mateu, C., Borri, E., Castro-Gomes, J., & Cabeza, L. F. (2026). Bibliometrics in artificial intelligence in biomimetic and bio-inspired technologies: mapping the research landscape and identifying knowledge gaps. Heliyon, 12(15), Article e45560. https://doi.org/10.1016/j.heliyon.2026.e45560

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, biomimetics, machine learning, bibliometrics, bio-inspired robotics, neuromorphic computing, thermal energy storage, renewable energy, swarm intelligence, phase change materials, keyword co-occurrence, research trends