The rise of intelligent matter
Open accessAbstract
Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.
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There is a rising interest in energy-efficient hardware primarily optimized for artificial intelligence.
“Therefore, there is a rising interest in energy-efficient hardware primarily optimized for artificial intelligence [29].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [29]
Cites
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825 · see citing works
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956 · see citing works
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568 · see citing works
In-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298 · see citing works
Stretchable silicon nanoribbon electronics for skin prosthesis
Kim, J., Lee, M., Shim, H. J., Ghaffari, R., Cho, H. R., Son, D., Jung, Y. H., Soh, M., Choi, C., Jung, S., et al. (2014) · Nature Communications · doi:10.1038/ncomms6747 · Cited by 1,435 · see citing works
Programmable self-assembly in a thousand-robot swarm
Rubenstein, M., Cornejo, A., & Nagpal, R. (2014) · Science · doi:10.1126/science.1254295 · Cited by 1,266 · see citing works
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 903 · see citing works
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898 · see citing works
Whitesides, G. M. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201800907 · Cited by 850 · see citing works
Kanao, K., Harada, S., Yamamoto, Y., Honda, W., Arie, T., Akita, S., & Takei, K. (2015) · RSC Advances · doi:10.1039/c5ra03110a · Cited by 148 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Technology Roadmap for Flexible Sensors
Luo, Y., Abidian, M. R., Ahn, J., Akinwande, D., Andrews, A. M., Antonietti, M., Bao, Z., Magnus Berggren, A., Berkey, C. A., Bettinger, C. J., et al. (2023) · ACS Nano · doi:10.1021/acsnano.2c12606 · Cited by 1,419 · see citing works
Liquid Crystals: Versatile Self-Organized Smart Soft Materials.
Bisoyi, H., & Li, Q. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00761 · Cited by 607 · see citing works
ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis
Zheng, Z.-L., Zhang, O.-F., Borgs, C., Chayes, J., & Yaghi, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c05819 · Cited by 484 · see citing works
Ultrasound-responsive systems as components for smart materials
Athanassiadis, A. G., Ma, Z., Moreno-Gomez, N., Melde, K., Choi, E., Goyal, R., & Fischer, P. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00622 · Cited by 293 · see citing works
Stimuli-Responsive Polymer-Based Nanosystems for Cancer Theranostics.
Wei, D.-S., Sun, Y., Zhu, H., & Fu, Q.-R. (2023) · ACS Nano · doi:10.1021/acsnano.3c06019 · Cited by 276 · see citing works
A guidance to intelligent metamaterials and metamaterials intelligence
Qian, C., Kaminer, I. E., & Chen, H. (2025) · Nature Communications · doi:10.1038/s41467-025-56122-3 · Cited by 267 · see citing works
“More fruitful interactions with fundamental science and technological innovation could unleash the full impact to the real-world applications in a more detailed but less noticeable manner than before 278 .”
Yang, M., Xu, Y.-Y., Zhang, X., Bisoyi, H., Xue, P., Yang, Y.-Z., Yang, X., Valenzuela, C., Chen, Y.-H., Wang, L., et al. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202201884 · Cited by 259
Materials with Tunable Optical Properties for Wearable Epidermal Sensing in Health Monitoring
Han, F., Wang, T., Liu, G., Liu, H., Xie, X., Wei, Z., Li, J., Jiang, C., He, Y., & Xu, F. (2022) · Advanced Materials · doi:10.1002/adma.202109055 · Cited by 250 · see citing works
Fatigue-free artificial ionic skin toughened by self-healable elastic nanomesh
Wang, J.-Q., Wu, B.-H., Wei, P., Sun, S.-T., & Wu, P.-Y. (2022) · Nature Communications · doi:10.1038/s41467-022-32140-3 · Cited by 239 · see citing works
Yang, L.-D., Jiang, J.-L., Gao, X., Wang, Q.-L., Dou, Q., & Zhang, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00482-8 · Cited by 228 · see citing works
“This step is necessary to make microrobot swarms evolve towards intelligent matter 37 .”
BackgroundFlexible Plasmonic Biosensors for Healthcare Monitoring: Progress and Prospects
Song, L., Chen, J., Xu, B. B., & Huang, Y. (2021) · ACS Nano · doi:10.1021/acsnano.1c07176 · Cited by 186 · see citing works
Mechanical Intelligent Energy Harvesting: From Methodology to Applications
Zhao, L.-C., Zou, H.-X., Wei, K.-X., Zhou, S.-X., Meng, G., & Zhang, W. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202300557 · Cited by 174 · see citing works
A dynamically reprogrammable surface with self-evolving shape morphing
Bai, Y., Wang, H., Xue, Y., Pan, Y., Kim, J.-T., Ni, X., Liu, T.-L., Yang, Y., Han, M., Huang, Y., et al. (2022) · Nature · doi:10.1038/s41586-022-05061-w · Cited by 168 · see citing works
Wu, Z., Wang, H., Ding, Q., Tao, K., Shi, W., Liu, C., Chen, J., & Wu, J. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202300046 · Cited by 162 · see citing works
Self-regulated non-reciprocal motions in single-material microstructures
Li, S.-C., Lerch, M. M., Waters, J. T., Deng, B., Martens, R. S., Yao, Y.-X., Kim, D. Y., Bertoldi, K., Grinthal, A., Balazs, A., et al. (2022) · Nature · doi:10.1038/s41586-022-04561-z · Cited by 151 · see citing works
Kim, H., Chang, C. S., Lee, S., Jiang, J., Jeong, J., Park, M., Meng, Y., Ji, J., Kwon, Y., Sun, X.-L., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00122-w · Cited by 150 · see citing works
Li, P., Su, N., Wang, Z.-Y., & Qiu, J.-S. (2021) · ACS Nano · doi:10.1021/acsnano.1c07186 · Cited by 149 · see citing works
Roadmap to Neuromorphic Computing with Emerging Technologies
Mehonic, A., Ielmini, D., Roy, K., Mutlu, O., Kvatinsky, S., Serrano-Gotarredona, T., Linares-Barranco, B., Spiga, S., Savel'ev, S., Balanov, A., et al. (2024) · APL Materials · doi:10.48550/arxiv.2407.02353 · Cited by 148
Self-citationReconfigurable in-sensor processing based on a multi-phototransistor–one-memristor array
Dang, B.-J., Zhang, T., Wu, X.-L., Liu, K.-Q., Huang, R., & Yang, Y.-C. (2024) · Nature Electronics · doi:10.1038/s41928-024-01280-3 · Cited by 147 · see citing works
“However, traditional vision computing systems, which are based on complementary metal–oxide–semiconductor (CMOS) technology using the von Neumann architecture, struggle with real-time image processing 4–12 .”
Mechanical integrated circuit materials
Helou, C. E., Grossmann, B., Tabor, C., Buskohl, P., & Harne, R. (2022) · Nature · doi:10.1038/s41586-022-05004-5 · Cited by 141
A review of soft manipulator research, applications, and opportunities
Chen, X.-Q., Zhang, X., Huang, Y.-Y., Cao, L., & Liu, J. (2021) · J. Field Robotics · doi:10.1002/rob.22051 · Cited by 141 · see citing works
“The smart material drive is actuated by the deformation of smart materials or structures through the change of voltage or current (Kaspar et al., 2021).”
BackgroundEmerging dynamic memristors for neuromorphic reservoir computing
Cao, J., Zhang, X., Cheng, H., Qiu, J., Liu, X., Wang, M., & Liu, Q. (2022) · Nanoscale · doi:10.1039/d1nr06680c · Cited by 134 · see citing works
Zhao, Y.-L., Wu, R., Hao, Y., Zhao, Y., Zhang, X., Liu, H., Zhai, W., Dai, K., Pan, C.-F., Liu, C.-T., et al. (2025) · Advances in Materials · doi:10.1002/adma.202507127 · Cited by 129 · see citing works
Liu, J., Huang, Y., Liu, Y.-Z., Zhang, D.-C., Koynov, K., Butt, H.-J., & Wu, S. (2024) · Nature Chemistry · doi:10.1038/s41557-024-01476-2 · Cited by 129 · see citing works
“These responsive hydrogels displayed various motions, such as bending, winding, rotation, folding and walking 25,26 .”
BackgroundMagnetic soft robots: design, actuation, and function
Wang, H., Zhu, Z.-S., Jin, H., Wei, R., Bi, L., & Zhang, W.-L. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.166219 · Cited by 110 · see citing works
Nature-inspired strategies for the synthesis of hydrogel actuators and their applications
Li, W., Guan, Q., Li, M., Saiz, E., & Hou, X. (2023) · Progress in Polymer Science · doi:10.1016/j.progpolymsci.2023.101665 · Cited by 142 · see citing works
Liquid crystal-based structural color actuators
Zhang, P., de Haan, L. D., Debije, M., & Schenning, A. (2022) · Light: Science & Applications · doi:10.1038/s41377-022-00937-y · Cited by 108
Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots.
Su, J.-T., He, K., Li, Y.-Z., Tu, J., & Chen, X.-D. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00906 · Cited by 106 · see citing works
Pattern recognition with neuromorphic computing using magnetic field–induced dynamics of skyrmions
Yokouchi, T., Sugimoto, S., Rana, B., Seki, S., Ogawa, N., Shiomi, Y., Kasai, S., & Otani, Y. (2022) · Science Advances · doi:10.1126/sciadv.abq5652 · Cited by 105 · see citing works
“Therefore, developing devices specialized for braininspired com puting, namely, neuromorphic devices, is highly required (4, 5).”
BackgroundKrauhausen, I., Coen, C.-T., Spolaor, S., Gkoupidenis, P., & van de Burgt, Y. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202307729 · Cited by 101 · see citing works
Jin, D.-D., & Zhang, L. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00619 · Cited by 101
Tao, D., Su, P., Chen, A., Gu, D., Eginligil, M., & Huang, W. (2025) · npj Flexible Electronics · doi:10.1038/s41528-024-00357-5 · Cited by 100 · see citing works
“On the other hand, in the era of the Internet of Things (IoT) and arti fi cial intelligence, a large amount of data have been accumulated, via mobile electronic devices and robots as mechanical energy-based self-powered systems 3,11 – 13 .”
Sustainable carbon materials in environmental and energy applications
Sun, Z., Liao, Y., Zhang, Y., Sun, S., Kan, Q., Wu, Z., Yu, L., Dong, Z., Wang, Z., He, R., et al. (2025) · Sustainable Carbon Materials · doi:10.48130/scm-0025-0002 · Cited by 51 · see citing works
Challenges and opportunities in 2D heterostructures for electronic and optoelectronic devices
Chakraborty, S., Kundu, B., Nayak, B., Dash, S., & Sahoo, P. (2022) · iScience · doi:10.1016/j.isci.2022.103942 · Cited by 94 · see citing works
“Several technologies have emerged, such as phase-change memory, resistive random access memory, and spintronic memory that simultaneously provides memory and logic units (Kaspar et al., 2021; Xia and Yang, 2019).”
Thermally trainable dual network hydrogels
Hu, S., Fang, Y.-H., Liang, C., Turunen, M., Ikkala, O., & Zhang, H. (2023) · Nature Communications · doi:10.1038/s41467-023-39446-w · Cited by 93 · see citing works
Nanomaterials for Flexible Neuromorphics
Ding, G., Li, H., Zhao, J., Zhou, K., Zhai, Y., Lv, Z., Zhang, M., Yan, Y., Han, S., & Zhou, Y. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00369 · Cited by 107 · see citing works
Gong, X., Ji, Y., Liu, X.-Y., Yu, J.-Y., Zhang, S.-C., & Ding, B. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202316030 · Cited by 92
Homeostatic neuro-metasurfaces for dynamic wireless channel management
Fan, Z., Qian, C., Jia, Y.-T., Wang, Z., Ding, Y., Wang, D.-P., Tian, L., Li, E.-P., Cai, T., Zheng, B., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn7905 · Cited by 90 · see citing works
“In particular, recent decades have witnessed an unprecedented promotion because of the big advances in the Internet of Things (IoT) and artificial intelligence (2).”
BackgroundToward a formal theory for computing machines made out of whatever physics offers
Jaeger, H., Noheda, B., & van der Wiel, W. G. (2023) · Nature Communications · doi:10.1038/s41467-023-40533-1 · Cited by 76 · see citing works
The amorphous state as a frontier in computational materials design
Liu, Y.-B., Madanchi, A., Anker, A., Simine, L., & Deringer, V. L. (2024) · Nature Reviews Materials · doi:10.1038/s41578-024-00754-2 · Cited by 87 · see citing works
Metareview: a survey of active matter reviews
Vrugt, M. T., & Wittkowski, R. (2024) · The European Physical Journal E : Soft matter · doi:10.1140/epje/s10189-024-00466-z · Cited by 87 · see citing works
“Finally, some reviews cover the development of intelligent matter [33,157] (materials possessing basic features of intelligence).”
Lightweight and Strong Cellulosic Triboelectric Materials Enabled by Cell Wall Nanoengineering.
Li, X., Wang, J., Liu, Y.-H., Zhao, T., Luo, B., Liu, T., Zhang, S., Chi, M., Cai, C.-C., Wei, Z., et al. (2024) · Nano letters (Print) · doi:10.1021/acs.nanolett.4c00458 · Cited by 84
Feedback-controlled hydrogels with homeostatic oscillations and dissipative signal transduction
Zhang, H., Zeng, H., Eklund, A., Guo, H.-S., Priimagi, A., & Ikkala, O. (2022) · Nature Nanotechnology · doi:10.1038/s41565-022-01241-x · Cited by 84 · see citing works
Biomimetic artificial neuromuscular fiber bundles with built-in adaptive feedback
Chen, Y.-H., Valenzuela, C., Liu, Y., Yang, X., Yang, Y.-Z., Zhang, X., Ma, S., Bi, R., Wang, L., & Feng, W. (2024) · Matter · doi:10.1016/j.matt.2024.10.022 · Cited by 83 · see citing works
Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration
Ren, L.-Q., Li, B.-Q., Wei, G.-W., Wang, K.-Y., Song, Z.-Y., Wei, Y.-Y., Ren, L., & Liu, Q.-P. (2021) · iScience · doi:10.1016/j.isci.2021.103075 · Cited by 83 · see citing works
Amiri, Z., Heidari, A., Jafari, N., & Hosseinzadeh, M. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100666 · Cited by 81 · see citing works
Encoding and Storage of Information in Mechanical Metamaterials
Meng, Z.-Q., Yan, H.-J., Liu, M.-C., Qin, W.-K., Genin, G., & Chen, C.-Q. (2023) · Advancement of science · doi:10.1002/advs.202301581 · Cited by 80
In-memory mechanical computing
Mei, T., & Chen, C.-Q. (2023) · Nature Communications · doi:10.1038/s41467-023-40989-1 · Cited by 79 · see citing works
“Thus, the historical data in fl uence subsequent decision-making in the mechanical system, which is a foundation of learning 10,11 .”
Light-steerable locomotion using zero-elastic-energy modes
Deng, Z., Li, K., Priimagi, A., & Zeng, H. (2024) · Nature Materials · doi:10.1038/s41563-024-02026-4 · Cited by 78 · see citing works
“2b and Supplementary Video 1).”
InfluentialMultifunctional Fiber‐Enabled Intelligent Health Agents
Chen, M., Li, P., Wang, R., Xiang, Y., Huang, Z., Yu, Q., He, M.-Y., Liu, J., Wang, J., Su, M., et al. (2022) · Advances in Materials · doi:10.1002/adma.202200985 · Cited by 74 · see citing works
Sun, Y., Xu, S., Xu, Z.-Q., Tian, J., Bai, M., Qi, Z., Niu, Y., Aung, H. H., Xiong, X., Han, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33118-x · Cited by 73 · see citing works
Materials for Dry Electrodes for the Electroencephalography: Advances, Challenges, Perspectives
Yang, L.-T., Liu, Q., Zhang, Z.-L., Gan, L., Zhang, Y., & Wu, J. (2021) · Advanced Materials & Technologies · doi:10.1002/admt.202100612 · Cited by 72 · see citing works
Fluidic innervation sensorizes structures from a single build material
Truby, R., Chin, L., Zhang, A., & Rus, D. (2022) · Science Advances · doi:10.1126/sciadv.abq4385 · Cited by 71 · see citing works
Counting and Sequential Information Processing in Mechanical Metamaterials.
Kwakernaak, L. J., & van Hecke, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.268204 · Cited by 69 · see citing works
Jiang, Y., Tan, P., Liu, X.-Q., & Sun, L.-B. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00555 · Cited by 69
Liquid metal droplets enabled soft robots
Zheng, L.-M., Handschuh‐Wang, S., Ye, Z., & Wang, B. (2022) · Applied Materials Today · doi:10.1016/j.apmt.2022.101423 · Cited by 68 · see citing works
Smart liquid crystal elastomer fibers
Ma, J.-Z., & Yang, Z.-Q. (2025) · Matter · doi:10.1016/j.matt.2024.101950 · Cited by 67 · see citing works
Fuel‐Driven and Enzyme‐Regulated Redox‐Responsive Supramolecular Hydrogels
Jain, M., & Ravoo, B. (2021) · Angewandte Chemie · doi:10.1002/anie.202107917 · Cited by 65 · see citing works
Self-citationSelf-regulated underwater phototaxis of a photoresponsive hydrogel-based phototactic vehicle
Hou, G., Zhang, X., Du, F.-H., Wu, Y.-D., Zhang, X., Lei, Z., Lu, W., Zhang, F.-Y., Yang, G., Wang, H.-M., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01490-4 · Cited by 64
Flytrap Inspired pH‐Driven 3D Hydrogel Actuator by Femtosecond Laser Microfabrication
Wang, J.-Y., Jin, F., Dong, X.-Y., Liu, J., & Zheng, M.-L. (2022) · Advanced Materials & Technologies · doi:10.1002/admt.202200276 · Cited by 64 · see citing works
Machine learning without a processor: Emergent learning in a nonlinear analog network
Dillavou, S., Beyer, B. D., Stern, M., Liu, A. J., Miskin, M. Z., & Durian, D. (2023) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2319718121 · Cited by 63 · see citing works
Intrinsic self-healing rubber: A review and perspective of material and reinforcement
Sani, N. F. M., Yee, H. J., Othman, N., Talib, A. A., & Shuib, R. (2022) · Polymer testing · doi:10.1016/j.polymertesting.2022.107598 · Cited by 63 · see citing works
Chemical reservoir computation in a self-organizing reaction network
Baltussen, M. G., de Jong, T. J., Duez, Q., Robinson, W. E., & Huck, W. T. S. (2024) · Nature · doi:10.1038/s41586-024-07567-x · Cited by 62 · see citing works
Controlled pathways and sequential information processing in serially coupled mechanical hysterons
Liu, J.-R., Teunisse, M. H., Korovin, G., Vermaire, I. R., Jin, L., Bense, H., & van Hecke, M. (2024) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2308414121 · Cited by 61
Triboiontronics for efficient energy and information flow
Li, X., Li, S.-X., Guo, X., Shao, J.-J., Wang, Z., & Wei, D. (2023) · Matter · doi:10.1016/j.matt.2023.08.022 · Cited by 60 · see citing works
Self-vectoring electromagnetic soft robots with high operational dimensionality
Li, W.-B., Chen, H.-Y., Yi, Z., Fang, F.-Y., Guo, X., Wu, Z.-Y., Gao, Q.-H., Shao, L., Xu, J., Meng, G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35848-y · Cited by 57 · see citing works
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
Bongard, J., & Levin, M. (2023) · Biomimetics · doi:10.3390/biomimetics8010110 · Cited by 55 · see citing works
“Indeed, their competency, plasticity, and autonomy [2,22,258,259] may enable a kind of second-order polycomputing, where various body components attempt to model each other’s computational behavior (in effect serving as observers) and act based on their expected reward, from their perspective.”
Superhydrophobic Triboelectric Structural Materials Enabled by Hierarchical Spatial Assembly
Hou, Z., Liu, T., Li, M., Xu, Y., Wu, X., Luo, S., Cai, C.-C., Mou, Y., Qiu, Y., Nie, S.-X., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202400750 · Cited by 52
Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning
Liu, Z.-L., Fang, H.-B., Xu, J., & Wang, K.-W. (2023) · Advancement of science · doi:10.1002/advs.202305146 · Cited by 52 · see citing works
A Robot Web for Distributed Many-Device Localization
Murai, R., Ortiz, J., Saeedi, S., Kelly, P., & Davison, A. (2022) · IEEE Transactions on robotics · doi:10.1109/tro.2023.3324127 · Cited by 52 · see citing works
“…moving systems is a key part of the concept of “intelligent matter,” where distribution and communication might be at the microscopic level to enable new classes of technology such as micromachines [26] which can self-organize in ways that might approach the capabilities of biological systems [29].”
BackgroundAutonomous nanorobots with powerful thrust under dry solid-contact conditions by photothermal shock
Gu, Z., Zhu, R., Shen, T., Dou, L., Liu, H.-J., Liu, Y.-F., Liu, X., Liu, J., Zhuang, S., & Gu, F. (2023) · Nature Communications · doi:10.1038/s41467-023-43433-6 · Cited by 49
“Furthermore, through spatial light modulation 24 and multi-robot collaboration, autonomous nanorobot swarms 41,42 can be realized to perform more challenging tasks and unearth unprecedented application scenarios in various fi elds such as nanoma-nufacturing, biomedicine 43,44 , and aerospace in the…”
Biological Robots: Perspectives on an Emerging Interdisciplinary Field
Blackiston, D., Kriegman, S., Bongard, J., & Levin, M. (2022) · Soft Robotics · doi:10.1089/soro.2022.0142 · Cited by 48 · see citing works
Soft robotics towards sustainable development goals and climate actions
Giordano, G., Babu, S. P. M., & Mazzolai, B. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1116005 · Cited by 47 · see citing works
“We believe that soft robots that are smart, biodegradable, and capable of couple sensing, actuation, computation, and communication have the potential to support SDGs 11, 13, 14, and 15 (McEvoy and Correll, 2015; Garrad et al., 2019; Kaspar et al., 2021; Meder et al., 2022).”
BackgroundMulti-stimulus perception and visualization by an intelligent liquid metal-elastomer architecture
Wang, H.-Z., Yuan, B., Zhu, X., Shan, X., Chen, S., Ding, W.-B., Cao, Y., Dong, K.-C., Zhang, X., Guo, R., et al. (2024) · Science Advances · doi:10.1126/sciadv.adp5215 · Cited by 46
Advancing interactive systems with liquid crystal network-based adaptive electronics
Lyu, P., Broer, D. J., & Liu, D.-Q. (2024) · Nature Communications · doi:10.1038/s41467-024-48353-7 · Cited by 46 · see citing works
“Additionally, centralized information processing encounters limitations when dealing with the vast amounts of data transmission and computational demands among different modules in dynamic environments 16,17 .”
Miao, J.-Q., & Sun, S. (2023) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2023.171160 · Cited by 42 · see citing works
An ultrawide-range photochromic molecular fluorescence emitter
Chen, X., Hou, X., Chen, X.-M., & Li, Q.-H. (2024) · Nature Communications · doi:10.1038/s41467-024-49670-7 · Cited by 41 · see citing works
“Arti fi cial intelligent materials are outstanding accomplishments made byhumanstoimitatehowlivingorganismsrespondtoexternalstimuli, especially optical materials that can be used to create colorful, ever-changingreal-timedisplays 1 – 3 .”
Su, Z., Zhao, Y.-J., Huang, Y.-Q., Xu, C., Yang, X., Wang, B.-R., Xu, B., Xu, S.-Z., & Bai, G. (2022) · Nano Reseach · doi:10.1007/s12274-022-4827-z · Cited by 41
Recent Development of Fibrous Hydrogels: Properties, Applications and Perspectives
Luo, W., Ren, L., Hu, B., Zhang, H., Yang, Z., Jin, L., & Zhang, D. (2024) · Advancement of science · doi:10.1002/advs.202408657 · Cited by 40 · see citing works
Magnetic skyrmions: Basic properties and potential applications
Li, S., Wang, X.-W., & Rasing, T. (2023) · Interdisciplinary Materials · doi:10.1002/idm2.12072 · Cited by 40 · see citing works
Bacterial cellulose/MWCNT coatings for highly sensitive and flexible paper-based humidity sensors
Ma, H.-L., Liu, Z.-Q., Lou, J., Ding, Q.-J., Jiang, Y.-F., Li, X., & Han, W.-J. (2022) · Cellulose · doi:10.1007/s10570-022-04960-5 · Cited by 40 · see citing works
Brain-inspired computing systems: a systematic literature review
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T., Kinge, S., & van der Wiel, W. G. (2024) · The European Physical Journal B · doi:10.1140/epjb/s10051-024-00703-6 · Cited by 39 · see citing works
Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.
Liu, Y., Fan, P., Pan, Y.-X., & Ping, J.-F. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00115 · Cited by 39 · see citing works
Zhou, N., Cui, T., Lei, Z.-Y., & Wu, P.-Y. (2025) · Nature Communications · doi:10.1038/s41467-025-59944-3 · Cited by 38 · see citing works
“Bridging this gap is critical to advance the development of intelligent soft materials that integrate perception, learning, and memory, offering transformative opportunities in wearable devices, soft robotics, and adaptable systems 10 .”
Intelligent electroactive material systems with self-adaptive mechanical memory and sequential logic
Helou, C. E., Hyatt, L. P., Buskohl, P. R., & Harne, R. L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317340121 · Cited by 38 · see citing works
Guo, T., Zhang, B., Wang, X.-Y., Xiao, Y., Sun, B., Zhou, Y. N., & Wu, Y.-M. A. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202303879 · Cited by 38 · see citing works
Xu, L., Xu, Y., Li, L., Qiu, C., Ling, Y., Hu, Y., Chang, L., Peng, Q.-Y., & He, X.-D. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202414338 · Cited by 37 · see citing works
Graphene nanoparticles as data generating digital materials in industry 4.0
Ali, M. A., Irfan, M. S., Khan, T., Khalid, M. Y., & Umer, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-31672-y · Cited by 40 · see citing works
Robo-Matter towards reconfigurable multifunctional smart materials
Wang, J., Wang, G., Chen, H.-C., Liu, Y.-P., Wang, P.-L., Yuan, D.-M., Ma, X.-Y., Xu, X.-Y., Cheng, Z.-D., Ji, B.-H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53123-6 · Cited by 35 · see citing works
“These developments suggest that robotic materials (or intelligent matter) with distributed information exchanging and feedback capability at the building-block level, can achieve smartness that signi fi cantly extends the functionality of classical smart materials 19,52,53 .”
BackgroundWang, B., Zhang, B.-F., Tan, Y., Ji, F., Lv, G., Pan, C., Handschuh‐Wang, S., Zhang, L., & Zhou, X.-C. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200080 · Cited by 35
Gravity-resisting colloidal collectives
Law, J., Chen, H., Wang, Y., Yu, J., & Sun, Y. (2022) · Science Advances · doi:10.1126/sciadv.ade3161 · Cited by 33
“Dynamic colloidal self-assembly is an essential means of creating functional materials and systems to enable applications in materials engineering such as the formation of intelligent matters (5), in chemical engineering such as catalysis (6, 7), in microfactories such as contactless material handling (8–10) and the c…”
BackgroundStimuli-responsive metamaterials with information-driven elastodynamics programming
Li, C., Peng, Z., & He, Q.-B. (2021) · Matter · doi:10.1016/j.matt.2021.11.031 · Cited by 33
Li, S.-C., Aizenberg, M., Lerch, M. M., & Aizenberg, J. (2023) · Accounts of Materials Research · doi:10.1021/accountsmr.3c00101 · Cited by 32 · see citing works
Penocchio, E., Bachir, A., Credi, A., Astumian, R. D., & Ragazzon, G. (2024) · Chem · doi:10.1016/j.chempr.2024.07.038 · Cited by 31 · see citing works
Memlumor: A Luminescent Memory Device for Energy-Efficient Photonic Neuromorphic Computing
Marunchenko, A., Kumar, J., Kiligaridis, A., Tatarinov, D., Pushkarev, A. P., Vaynzof, Y., & Scheblykin, I. (2024) · ACS Energy Letters · doi:10.1021/acsenergylett.4c00691 · Cited by 31 · see citing works
AI-driven solutions in wastewater treatment and agricultural reuse systems: A comprehensive review.
Liu, Y., Mancuso, G., Petrotto, L., Lavrnić, S., Dong, Z., Tian, Y., Zhang, J., & Toscano, A. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127008 · Cited by 30 · see citing works
Cellulose‐Templated Nanomaterials for Nanogenerators and Self‐Powered Sensors
Qian, W.-Q., & Yang, Y. (2024) · Advances in Materials · doi:10.1002/adma.202412858 · Cited by 30 · see citing works
Chen, H., Zhou, T., Li, S., Feng, J.-Y., Li, W., Li, L., Zhou, X., Wang, M., Li, F., Zhao, X.-Q., et al. (2023) · ACS Applied Materials and Interfaces · doi:10.1021/acsami.3c09690 · Cited by 30 · see citing works
Recent Advances in Bioelectronics for Localized Drug Delivery
Jiang, X.-R., Wu, H., Xiao, A., Huang, Y., Yu, X.-G., & Chang, L.-Q. (2023) · Small Methods · doi:10.1002/smtd.202301068 · Cited by 30 · see citing works
Carbon Nanomaterials (CNMs) and Enzymes: From Nanozymes to CNM-Enzyme Conjugates and Biodegradation
Rozhin, P., Gamal, J. A. M., Giordani, S., & Marchesan, S. (2022) · Materials · doi:10.3390/ma15031037 · Cited by 30
“The rise of smart materials that can respond and adapt to stimuli and changes in the local microenvironment has opened new avenues that are enabling great progress especially in the biomedical field [385].”
Background
- Authors
- Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P.
- Affiliations
- University of Münster (DE)
- Corresponding author
- Pernice, W. H. P.
- Venue
- Nature
- Published
- 2021-06-16
- Volume
- 594
- Issue
- 7863
- Pages
- 345-355
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3169707058
- Cited by
- 526
- References
- 0
- Citations by year
- 2026: 70 · 2025: 136 · 2024: 112 · 2023: 106 · 2022: 75 · 2021: 27
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- Funders
- European Commission (101017237, HORIZON2020)
- Deutsche Forschungsgemeinschaft (SFB 1459, 433682494)
- Volkswagen Foundation (A126874)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- neuromorphic computing, decentralized computing, molecular systems, soft materials, solid-state materials, soft robotics, distributed memory, pattern recognition
- MeSH terms
- Artificial Intelligence, Colloids, Environment, Enzymes, Equipment Design, Homeostasis, Humans, Physical Stimulation, Robotics, Skin, Artificial, Biomimetics, Biomimetic Materials
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