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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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“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]
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Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
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
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
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
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
Programmable self-assembly in a thousand-robot swarm
Rubenstein, M., Cornejo, A., & Nagpal, R. (2014) · Science · doi:10.1126/science.1254295 · Cited by 1,308
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 899
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
Whitesides, G. M. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201800907 · Cited by 850
Research works printed but not linked: 105
- Sternberg, R. J. Handbook of Intelligence (Cambridge Univ. Press, 2000). · DOI
- Sternberg, R. J. Theories of intelligence. In APA Handbook of Giftedness and Talent (eds Pfeiffer, S. I. et al.) 145–161 (American Psychological Association, 2018). · DOI
- Legg, S. & Hutter, M. Universal intelligence: a definition of machine intelligence. Minds Mach. 17, 391–444 (2007). · DOI · Paper page
- Lane, N. D., Bhattacharya, S., Mathur, A., Forlivesi, C. & Kawsar, F. Squeezing deep learning into mobile and embedded devices. IEEE Pervasive Comput. 16, 82–88 (2017). · DOI
- Hecht, J. Lidar for self-driving cars. Opt. Photonics News 29, 26–33 (2018). · DOI
- Fernández-Caramés, T. M. & Fraga-Lamas, P. Towards the internet-of-smart-clothing: a review on IoT wearables and garments for creating intelligent connected E-textiles. Electronics 7, 405 (2018). · DOI
- Majidi, C. Soft robotics: a perspective—current trends and prospects for the future. Soft Robot. 1, 5–11 (2014). · DOI
- Hamdioui, S. et al. Applications of computation-in-memory architectures based on memristive devices. In Proc. 2019 Design, Automation and Test in Europe Conference and Exhibition 486–491, https://doi.org/10.23919/DATE.2019.8715020 (2019). · DOI
- Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020). · DOI · Paper page
- Wegst, U. G. K., Bai, H., Saiz, E., Tomsia, A. P. & Ritchie, R. O. Bioinspired structural materials. Nat. Mater. 14, 23–36 (2015). · DOI
- Isaacoff, B. P. & Brown, K. A. Progress in top-down control of bottom-up assembly. Nano Lett. 17, 6508–6510 (2017). · DOI
- McEvoy, M. A. & Correll, N. Materials that couple sensing, actuation, computation, and communication. Science 347, 1261689 (2015). · DOI
- Walther, A. Viewpoint: from responsive to adaptive and interactive materials and materials systems: a roadmap. Adv. Mater. 32, 1905111 (2020). · DOI
- Merindol, R. & Walther, A. Materials learning from life: concepts for active, adaptive and autonomous molecular systems. Chem. Soc. Rev. 46, 5588–5619 (2017). · DOI
- Urban, M. W. Handbook of Stimuli-Responsive Materials (Wiley, 2011). · DOI
- He, X. et al. Synthetic homeostatic materials with chemo-mechano-chemical self-regulation. Nature 487, 214–218 (2012). An intriguing example of an autonomous, homeostatic material system based on chemo-mechanical feedback loops. · DOI
- Anderson, C., Theraulaz, G. & Deneubourg, J. L. Self-assemblages in insect societies. Insectes Soc. 49, 99–110 (2002). · DOI
- Lopez, U., Gautrais, J., Couzin, I. D. & Theraulaz, G. From behavioural analyses to models of collective motion in fish schools. Interface Focus 2, 693–707 (2012). · DOI
- Bajec, I. L. & Heppner, F. H. Organized flight in birds. Anim. Behav. 78, 777–789 (2009). · DOI
- Hinchey, M. G., Sterritt, R. & Rouff, C. Swarms and swarm intelligence. Computer 40, 111–113 (2007). · DOI
- Yu, J., Wang, B., Du, X., Wang, Q. & Zhang, L. Ultra-extensible ribbon-like magnetic microswarm. Nat. Commun. 9, 3260 (2018). This article demonstrates how paramagnetic nanoparticles self-organize in a microswarm that can pass obstacles and how its locomotion can be controlled by applying oscillating magnetic fields. · DOI
- Palacci, J., Sacanna, S., Steinberg, A. P., Pine, D. J. & Chaikin, P. M. Living crystals of light-activated colloidal surfers. Science 339, 936–940 (2013). · DOI
- Yan, J., Bloom, M., Bae, S. C., Luijten, E. & Granick, S. Linking synchronization to self-assembly using magnetic Janus colloids. Nature 491, 578–581 (2012). · DOI
- Liang, X. et al. Hierarchical microswarms with leader–follower-like structures: electrohydrodynamic self-organization and multimode collective photoresponses. Adv. Funct. Mater. 30, 1908602 (2020). · DOI
- Mou, F. et al. Phototactic flocking of photochemical micromotors. iScience 19, 415–424 (2019). This study shows flocking behaviour of synthesized spherical microparticles, which can execute transporting tasks along predefined pathways or bypass obstacles. · DOI
- Dai, B. et al. Programmable artificial phototactic microswimmer. Nat. Nanotechnol. 11, 1087–1092 (2016). · DOI
- Tagliazucchi, M., Weiss, E. A. & Szleifer, I. Dissipative self-assembly of particles interacting through time-oscillatory potentials. Proc. Natl Acad. Sci. USA 111, 9751–9756 (2014). · DOI
- Carnall, J. M. A. et al. Mechanosensitive self-replication driven by self-organization. Science 327, 1502–1506 (2010). · DOI
- Sadownik, J. W., Mattia, E., Nowak, P. & Otto, S. Diversification of self-replicating molecules. Nat. Chem. 8, 264–269 (2016). · DOI · Paper page
- Monreal Santiago, G., Liu, K., Browne, W. R. & Otto, S. Emergence of light-driven protometabolism upon recruitment of a photocatalytic cofactor by a self-replicator. Nat. Chem. 12, 603–607 (2020). · DOI
- Zhu, B. et al. Skin-inspired haptic memory arrays with an electrically reconfigurable architecture. Adv. Mater. 28, 1559–1566 (2016). · DOI
- Son, D. et al. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat. Nanotechnol. 9, 397–404 (2014). · DOI · Paper page
- Miriyev, A., Stack, K. & Lipson, H. Soft material for soft actuators. Nat. Commun. 8, 596 (2017). · DOI · Paper page
- Zhao, Z., Wang, C., Yan, H. & Liu, Y. Soft robotics programmed with double crosslinking DNA hydrogels. Adv. Funct. Mater. 29, 1905911 (2019). This article shows impressively how to translate nanometre-scale DNA self-assembly into macroscopic movements of soft materials, an encouraging achievement for soft robotics. · DOI
- Yang, H. et al. 3D printed photoresponsive devices based on shape memory composites. Adv. Mater. 29, 1701627 (2017). · DOI · Paper page
- Lai, Y. C. et al. Actively perceiving and responsive soft robots enabled by self-powered, highly extensible, and highly sensitive triboelectric proximity- and pressure-sensing skins. Adv. Mater. 30, 1801114 (2018). This work presents soft robots driven by self-generated electricity via the triboelectric effect, which can sense and embrace close objects. · DOI
- Schroeder, T. B. H. et al. An electric-eel-inspired soft power source from stacked hydrogels. Nature 552, 214–218 (2017). · DOI
- Liu, Y. et al. Stretchable motion memory devices based on mechanical hybrid materials. Adv. Mater. 29, 1701780 (2017). · DOI
- Oh, J. Y. et al. Intrinsically stretchable and healable semiconducting polymer for organic transistors. Nature 539, 411–415 (2016). · DOI
- Urban, M. W. et al. Key-and-lock commodity self-healing copolymers. Science 225, 220–225 (2018). A remarkable example for an advanced soft material with self-healing capabilities. · DOI
- Chen, Y., Kushner, A. M., Williams, G. A. & Guan, Z. Multiphase design of autonomic self-healing thermoplastic elastomers. Nat. Chem. 4, 467–472 (2012). · DOI
- Li, C. H. et al. A highly stretchable autonomous self-healing elastomer. Nat. Chem. 8, 618–624 (2016). · DOI
- Beyer, H. M. et al. Synthetic biology makes polymer materials count. Adv. Mater. 30, 1800472 (2018). · DOI
- Zhang, H., Zeng, H., Priimagi, A. & Ikkala, O. Programmable responsive hydrogels inspired by classical conditioning algorithm. Nat. Commun. 10, 3267 (2019). · DOI
- Zeng, H., Zhang, H., Ikkala, O. & Priimagi, A. Associative learning by classical conditioning in liquid crystal network actuators. Matter 2, 194–206 (2020). Associative learning is realized in a liquid crystal network material via a conditioning process, where an initially neutral light stimulus is asscociated with heating. · DOI
- Kumar, B. V. V. S. P., Patil, A. J. & Mann, S. Enzyme-powered motility in buoyant organoclay/DNA protocells. Nat. Chem. 10, 1154–1163 (2018). · DOI
- Garrad, M., Soter, G., Conn, A. T., Hauser, H. & Rossiter, J. A soft matter computer for soft robots. Sci. Robot. 4, eaaw6060 (2019). The authors propose a computational system integrated into a soft material, which, inspired by biological systems, tranfers information via a fluid perfusing through the system. · DOI
- Miller, J. F. & Downing, K. Evolution in materio: looking beyond the silicon box. In Proc. NASA/DoD Conference on Evolvable Hardware 167–176, https://doi.org/10.1109/EH.2002.1029882 (2002). · DOI
- Feynman, R. P. The Character of Physical Law (MIT Press, 1967).
- Yoshihito, A. Information processing using intelligent materials - information-processing architectures for material processors. J. Intell. Mater. Syst. Struct. 5, 418–423 (1994). · DOI
- Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014). · DOI
- Zhang, W., Mazzarello, R., Wuttig, M. & Ma, E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat. Rev. Mater. 4, 150–168 (2019). · DOI
- Sebastian, A. et al. Tutorial: brain-inspired computing using phase-change memory devices. J. Appl. Phys. 124, 111101 (2018). · DOI
- Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019). · DOI
- Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photon. 9, 725–732 (2015). · DOI
- Cheng, Z., Ríos, C., Pernice, W. H. P., David Wright, C. & Bhaskaran, H. On-chip photonic synapse. Sci. Adv. 3, e1700160 (2017). This article shows an artificial synapse consisting of a photonic waveguide and a phase-change material, which paves the way for on-chip neuromorphic computing. · DOI
- Gupta, A., Sakthivel, T. & Seal, S. Recent development in 2D materials beyond graphene. Prog. Mater. Sci. 73, 44–126 (2015). · DOI
- Novoselov, K. S., Mishchenko, A., Carvalho, A. & Castro Neto, A. H. 2D materials and van der Waals heterostructures. Science 353, aac9439 (2016). · DOI
- Geim, A. K. & Grigorieva, I. V. Van der Waals heterostructures. Nature 499, 419–425 (2013). · DOI
- Mak, K. F., Lee, C., Hone, J., Shan, J. & Heinz, T. F. Atomically thin MoS2: a new direct-gap semiconductor. Phys. Rev. Lett. 105, 136805 (2010). · DOI · Paper page
- Splendiani, A. et al. Emerging photoluminescence in monolayer MoS2. Nano Lett. 10, 1271–1275 (2010). · DOI
- Wan, J. et al. Tuning two-dimensional nanomaterials by intercalation: materials, properties and applications. Chem. Soc. Rev. 45, 6742–6765 (2016). · DOI
- Zeng, M. et al. Bandgap tuning of two-dimensional materials by sphere diameter engineering. Nat. Mater. 19, 528–533 (2020). · DOI
- Choi, C. et al. Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array. Nat. Commun. 8, 1664 (2017). · DOI
- Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018). · DOI
- He, C. et al. Artificial synapse based on van der Waals heterostructures with tunable synaptic functions for neuromorphic computing. ACS Appl. Mater. Interfaces 12, 11945–11954 (2020). · DOI
- Park, H., Mastro, M. A., Tadjer, M. J. & Kim, J. Programmable multilevel memtransistors based on van der Waals heterostructures. Adv. Electron. Mater. 5, 1900333 (2019). · DOI
- Liu, C. et al. A semi-floating gate memory based on van der Waals heterostructures for quasi-non-volatile applications. Nat. Nanotechnol. 13, 404–410 (2018). · DOI
- Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). The presented image sensor based on a 2D material constitutes at the same time an artificial neural network. · DOI
- Bose, S. K. et al. Evolution of a designless nanoparticle network into reconfigurable Boolean logic. Nat. Nanotechnol. 10, 1048–1052 (2015). Computational functionality is experimentally realized in a disordered nanomaterial network consisting of arbitrarily interconnected, functionalized nanoparticles. · DOI
- Chen, T. et al. Classification with a disordered dopant-atom network in silicon. Nature 577, 341–345 (2020). · DOI · Paper page
- Ruiz Euler, H.-C. et al. A deep-learning approach to realising functionality in nanoelectronic devices. Nat. Nanotechnol. 15, 992–998 (2020). · DOI · Paper page
- Ruiz Euler, H.-C. et al. Dopant network processing units: towards efficient neural-network emulators with high-capacity nanoelectronic nodes. Preprint at http://arxiv.org/abs/2007.12371 (2020). · arXiv
- Estakhri, N. M., Edwards, B. & Engheta, N. Inverse-designed metastructures that solve equations. Science 363, 1333–1338 (2019). · DOI
- Hirano, Y., Segawa, Y., Kuroda-Sowa, T., Kawai, T. & Matsumoto, T. Conductance with stochastic resonance in Mn12 redox network without tuning. Appl. Phys. Lett. 104, 233104 (2014). · DOI
- Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002). · DOI · Paper page
- Steil, J. J. Backpropagation-decorrelation: online recurrent learning with O(N) complexity. In IEEE Int. Conf. on Neural Networks 2, 843–848 (IEEE, 2004). · DOI
- Schürmann, F., Meier, K. & Schemmel, J. Edge of chaos computation in mixed-mode VLSI—a hard liquid. In Advances in Neural Information Processing Systems 17, 1201–1208 (2004).
- Schrauwen, B., D’Haene, M., Verstraeten, D. & Van Campenhout, J. Compact hardware liquid state machines on FPGA for real-time speech recognition. Neural Netw. 21, 511–523 (2008). · DOI
- Fernando, C. & Sojakka, S. Pattern recognition in a bucket. In Proc. ECAL 588–597 (2003). · DOI
- Jones, B., Stekel, D., Rowe, J. & Fernando, C. Is there a liquid state machine in the bacterium Escherichia coli? In Proc. 2007 IEEE Symp. Artif. Life (CI-ALife 2007) 187–191, https://doi.org/10.1109/ALIFE.2007.367795 (2007). · DOI
- Dai, X. in Advances in Neural Networks Vol. 3174 (eds Yin, F. L. et al.) 519–524 (Springer, 2004). · DOI
- Goudarzi, A., Lakin, M. R. & Stefanovic, D. DNA reservoir computing: a novel molecular computing approach. In DNA Computing and Molecular Programming (eds Soloveichik D. & Yurke, B.) Vol. 8141, 76–89 (Springer, 2013). · DOI
- Nikolić, D., Haeusler, S., Singer, W. & Maass, W. Temporal dynamics of information content carried by neurons in the primary visual cortex. In Advances in Neural Information Processing Systems 1041–1048, https://doi.org/10.7551/mitpress/7503.003.0135 (2007). · DOI
- Duport, F., Smerieri, A., Akrout, A., Haelterman, M. & Massar, S. Fully analogue photonic reservoir computer. Sci. Rep. 6, 22381 (2016). · DOI
- Larger, L. et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt. Express 20, 3241 (2012). · DOI
- Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014). · DOI
- Larger, L. et al. High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification. Phys. Rev. X 7, 011015 (2017).
- Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). · DOI
- Kulkarni, M. S. Memristor-based reservoir computing. In 2012 IEEE/ACM Int. Symp. on Nanoscale 226–232, https://doi.org/10.1145/2765491.2765531 (IEEE/ACM, 2012). · DOI
- Bürger, J. & Teuscher, C. Variation-tolerant computing with memristive reservoirs. In 2013 IEEE/ACM Int. Symp. on Nanoscale Architectures (NANOARCH) 1–6, https://doi.org/10.1109/NanoArch.2013.6623028 (IEEE/ACM, 2013). · DOI
- Merkel, C., Saleh, Q., Donahue, C. & Kudithipudi, D. Memristive reservoir computing architecture for epileptic seizure detection. Proc. Comput. Sci. 41, 249–254 (2014). · DOI
- Hassan, A. M., Li, H. H. & Chen, Y. Hardware implementation of echo state networks using memristor double crossbar arrays. In 2017 Int. Joint Conf. on Neural Networks (IJCNN) 2171–2177, https://doi.org/10.1109/IJCNN.2017.7966118 (IEEE, 2017). · DOI
- Soures, N., Hays, L. & Kudithipudi, D. Robustness of a memristor based liquid state machine. In 2017 Int. Joint Conf. on Neural Networks (IJCNN) 2414–2420, https://doi.org/10.1109/IJCNN.2017.7966149 (IEEE, 2017). · DOI
- Du, C. et al. Reservoir computing using dynamic memristors for temporal information processing. Nat. Commun. 8, 2204 (2017). · DOI
- Moon, J. et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat. Electron. 2, 480–487 (2019). · DOI · Paper page
- Sillin, H. O. et al. A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing. Nanotechnology 24, 384004 (2013). · DOI
- Demis, E. C. et al. Atomic switch networks—nanoarchitectonic design of a complex system for natural computing. Nanotechnology 26, 204003 (2015). · DOI
- Demis, E. C. et al. Nanoarchitectonic atomic switch networks for unconventional computing. Jpn. J. Appl. Phys. 55, 1102B2 (2016). · DOI
- Dale, M., Stepney, S., Miller, J. F. & Trefzer, M. Reservoir computing in materio: an evaluation of configuration through evolution. In 2016 IEEE Symp. Ser. Comput. Intell. SSCI 2016 https://doi.org/10.1109/SSCI.2016.7850170 (IEEE, 2016). · DOI
- Dale, M., Miller, J. F. & Stepney, S. Reservoir computing as a model for in-materio computing. In Advances in Unconventional Computing (ed. Adamatzky, A.) 533–571 (Springer, 2017). · DOI
- Tanaka, H. et al. A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate. Nat. Commun. 9, 2693 (2018). · DOI
- Appeltant, L., Van Der Sande, G., Danckaert, J. & Fischer, I. Constructing optimized binary masks for reservoir computing with delay systems. Sci. Rep. 4, 3629 (2015). · DOI
- Dale, M., Miller, J. F., Stepney, S. & Trefzer, M. A. Evolving Carbon nanotube reservoir computers. In Unconventional Computation and Natural Computation (eds Amos, M. & Condon, A.) 49–61 (Springer, 2016). This study demonstrates how physical media can be exploited as a reservoir for machine-learning capabilities. · DOI
- Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). · DOI · Paper page
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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
“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., Zhang, X., Huang, Y., Cao, L., & Liu, J. (2021) · Journal of Field Robotics · doi:10.1002/rob.22051 · Cited by 147
“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
Zhao, Y., Wu, R. Y., Hao, Y., Zhao, Y. D., Zhang, X., Liu, H., Zhai, W., Dai, K., Pan, C., Liu, C., et al. (2025) · Advanced Materials · doi:10.1002/adma.202507127 · Cited by 106
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
“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., Jin, H., Wei, R., Bi, L., & Zhang, W. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.166219 · Cited by 128
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 144
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., He, K., Li, Y., Tu, J., & Chen, X. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00906 · Cited by 121
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 115
“Therefore, developing devices specialized for braininspired com puting, namely, neuromorphic devices, is highly required (4, 5).”
BackgroundKrauhausen, I., Coen, C., Spolaor, S., Gkoupidenis, P., & van de Burgt, Y. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202307729 · Cited by 126
Jin, D., & Zhang, L. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00619 · Cited by 126
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 109
“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
Challenges and opportunities in 2D heterostructures for electronic and optoelectronic devices
Chakraborty, S. K., Kundu, B., Nayak, B., Dash, S. P., & Sahoo, P. K. (2022) · iScience · doi:10.1016/j.isci.2022.103942 · Cited by 125
“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
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 109
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., Wang, Z., Ding, Y., Wang, D., Tian, L., Li, E., Cai, T., Zheng, B., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn7905 · Cited by 102
“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 84
The amorphous state as a frontier in computational materials design
Liu, Y., Madanchi, A., Anker, A. S., Simine, L., & Deringer, V. L. (2024) · Nature Reviews Materials · doi:10.1038/s41578-024-00754-2 · Cited by 97
Metareview: a survey of active matter reviews
Vrugt, M. T., & Wittkowski, R. (2025) · The European Physical Journal E · doi:10.1140/epje/s10189-024-00466-z · Cited by 82
“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., Zhao, T., Luo, B., Liu, T., Zhang, S., Chi, M., Cai, C., Wei, Z., et al. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c00458 · Cited by 89
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
Biomimetic artificial neuromuscular fiber bundles with built-in adaptive feedback
Chen, Y., Valenzuela, C., Liu, Y., Yang, X. J., Yang, J., Zhang, X., Ma, S., Bi, R., Wang, L., & Feng, W. (2024) · Matter · doi:10.1016/j.matt.2024.10.022 · Cited by 80
Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration
Ren, L., Li, B., Wei, G., Wang, K., Song, Z., Wei, Y., Ren, L., & Liu, Q. (2021) · iScience · doi:10.1016/j.isci.2021.103075 · Cited by 95
Amiri, Z., Heidari, A., Jafari, N., & Hosseinzadeh, M. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100666 · Cited by 81
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. (2023) · Nature Communications · doi:10.1038/s41467-023-40989-1 · Cited by 83
“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
“2b and Supplementary Video 1).”
InfluentialMultifunctional Fiber‐Enabled Intelligent Health Agents
Chen, M., Li, P., Wang, R., Xiang, Y., Huang, Z., Qiao, Y., He, M., Liu, J., Wang, J., Su, M., et al. (2022) · Advanced Materials · doi:10.1002/adma.202200985 · Cited by 89
Sun, Y., Xu, S., Xu, Z., 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 79
Materials for Dry Electrodes for the Electroencephalography: Advances, Challenges, Perspectives
Yang, L., Liu, Q., Zhang, Z., Gan, L., Zhang, Y., & Wu, J. (2021) · Advanced Materials Technologies · doi:10.1002/admt.202100612 · Cited by 78
Fluidic innervation sensorizes structures from a single build material
Truby, R. L., Chin, L., Zhang, A., & Rus, D. L. (2022) · Science Advances · doi:10.1126/sciadv.abq4385 · Cited by 72
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
Jiang, Y., Tan, P., Liu, X., & Sun, L. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00555 · Cited by 78
Liquid metal droplets enabled soft robots
Zheng, L., Handschuh‐Wang, S., Ye, Z., & Wang, B. (2022) · Applied Materials Today · doi:10.1016/j.apmt.2022.101423 · Cited by 81
Smart liquid crystal elastomer fibers
Ma, J., & Yang, Z. (2025) · Matter · doi:10.1016/j.matt.2024.101950 · Cited by 73
Fuel‐Driven and Enzyme‐Regulated Redox‐Responsive Supramolecular Hydrogels
Jain, M., & Ravoo, B. J. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202107917 · Cited by 98
Self-citationSelf-regulated underwater phototaxis of a photoresponsive hydrogel-based phototactic vehicle
Hou, G., Zhang, X., Du, F., Ya, W., Zhang, X. Y., Lei, Z., Lu, W., Zhang, F., Yang, G., Wang, H., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01490-4 · Cited by 69
Flytrap Inspired pH‐Driven 3D Hydrogel Actuator by Femtosecond Laser Microfabrication
Wang, J., Jin, F., Dong, X., Liu, J., & Zheng, M. (2022) · Advanced Materials Technologies · doi:10.1002/admt.202200276 · Cited by 70
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
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
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 64
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
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
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
Bongard, J., & Levin, M. G. (2023) · Biomimetics · doi:10.3390/biomimetics8010110 · Cited by 75
“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., Fang, H., Xu, J., & Wang, K. (2023) · Advanced Science · doi:10.1002/advs.202305146 · Cited by 44
A Robot Web for Distributed Many-Device Localization
Murai, R., Ortiz, J., Sajad Saeedi, G., Kelly, P. H. J., & Davison, A. J. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3324127 · Cited by 43
“…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. J., Kriegman, S., Bongard, J., & Levin, M. G. (2023) · Soft Robotics · doi:10.1089/soro.2022.0142 · Cited by 57
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 61
“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., Yuan, B., Zhu, X., Shan, X., Chen, S., Ding, W., Cao, Y., Dong, K., Zhang, X., Guo, R., et al. (2024) · Science Advances · doi:10.1126/sciadv.adp5215 · Cited by 59
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
“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., & Sun, S. (2023) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2023.171160 · Cited by 48
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
“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) · Advanced Science · doi:10.1002/advs.202408657 · Cited by 38
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
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
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
Flexible Microinterventional Sensors for Advanced Biosignal Monitoring
Liu, Y., Fan, P., Pan, Y., & Ping, J. F. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00115 · Cited by 41
Zhou, N., Cui, T., Lei, Z.-Y., & Wu, P.-Y. (2025) · Nature Communications · doi:10.1038/s41467-025-59944-3 · Cited by 38
“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
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
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
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
Robo-Matter towards reconfigurable multifunctional smart materials
Wang, J., Gao, W., Chen, H., Liu, Y., Wang, P., Yuan, D., Ma, X., Xu, X., Cheng, Z., Ji, B., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53123-6 · Cited by 37
“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., Tan, Y., Ji, F., Lv, G., Pan, C., Handschuh‐Wang, S., Zhang, L., & Zhou, X. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200080 · Cited by 41
Gravity-resisting colloidal collectives
Law, J., Chen, H., Wang, Y., Yu, J., & Sun, Y. (2022) · Science Advances · doi:10.1126/sciadv.ade3161 · Cited by 44
“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. (2021) · Matter · doi:10.1016/j.matt.2021.11.031 · Cited by 39
Li, S., Aizenberg, M., Lerch, M. M., & Aizenberg, J. (2023) · Accounts of Materials Research · doi:10.1021/accountsmr.3c00101 · Cited by 38
Penocchio, E., Bachir, A., Credi, A., Astumian, R. D., & Ragazzon, G. (2024) · Chem · doi:10.1016/j.chempr.2024.07.038 · Cited by 31
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
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
Cellulose‐Templated Nanomaterials for Nanogenerators and Self‐Powered Sensors
Qian, W., & Yang, Y. F. (2024) · Advanced Materials · doi:10.1002/adma.202412858 · Cited by 30
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
Recent Advances in Bioelectronics for Localized Drug Delivery
Jiang, X., Wu, H., Xiao, A., Huang, Y., Yu, X., & Chang, L. (2023) · Small Methods · doi:10.1002/smtd.202301068 · Cited by 28
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 39
“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
Metadata
- 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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