About the workshop

Objective

The objective of this workshop is to harness collective intelligence to formulate future topics and approaches for the emerging field of Intelligent Soft Matter.

Workshop Focus

A hypothetical Intelligent Soft Matter material could be created using a combination of self-assembling polymers, conductive polymers, magnetic particles, etc. This material would be able to adapt its structure and properties in response to external stimuli, collect and process sensory data, learn from its experiences, and make decisions based on stored memory and current sensory data as an autonomous agent or a swarm of agents.

Workshop format

This workshop has unusual format of a mixture of talks, free time and interactive sessions. It aims to integrate the diverse existing fields through collaborative brainstorming and lateral thinking, which involves approaching problems from new and different perspectives. By fostering such interdisciplinary dialogue, we hope to formulate the foundational principles of Intelligent Soft Matter and identify key priority topics for future research. This collaborative effort seeks to uncover novel insights and applications, ultimately establishing Intelligent Soft Matter as a recognized and impactful field of study.

Workshop structure

  • Invited and contributed talks of 20 min (7-8 speakers per day)
  • Morning and evening sessions
  • Free time (middle of the day) to mix with attendees
  • Discussions and collaboration sessions with hands-on sessions with collaborative writing (to define yet)

Introducing elements of Decentralized Science (DeSci) build on Open Science movement, where the collaboration goes across barriers of institutions, which is contrary to centralized approaches  prioritizing infrastructure, institutional resources and management.

Presenting alternative funding schemes focusing on valorizing confirmed scientific impact retroactively rather than funding proposals or institutions.

Protection of authorship with nanopublications in collective effort to generate (and harvest) ideas and define topics for future collaboration.

Outlook

The field of Intelligent Soft Matter is still in its early stages. It is an emerging area that encompasses various topics within soft matter science, such as adaptive materials, self-organization, self-assembly, active matter, soft matter theory, and soft robots and sensors.

Potential topics for discussion

  1. Sensory functionality refers to the capability of materials to act as sensors by detecting and quantifying external stimuli. For instance, the gel phase material itself functions as a sensor, with its swelling behavior providing crucial information about the nature and magnitude of the stimulus. This material can measure the degree of swelling through various techniques, such as observing changes in volume, electrical conductivity, or optical properties.
  2. Data Encoding: The measured sensory data is encoded into physical parameters that represent the stimulus intensity and characteristics. This encoding can be achieved by converting the measured changes in volume, conductivity, or optical properties into quantitative values that can be processed and analyzed.
  3. Memory: The material stores information about past stimuli and responses, allowing it to reference previous experiences. This memory function is crucial for recognizing patterns, learning from past interactions, and improving future responses based on accumulated knowledge.
  4. Learning involve the material’s ability to establish a database of stimulus-response patterns based on identified correlations over time.
    • Internal Feedback: The material compares the measured sensory data with the desired response stored in its memory. By evaluating the similarity between the actual response and the desired response, the material can assess the effectiveness of its previous actions. This internal feedback mechanism enables the material to refine its responses and improve its performance over time.
    • External Feedback: The material can also incorporate external feedback mechanisms, such as additional sensors or actuators, to gather real-time information about the environment and the effectiveness of its responses. This external feedback allows the material to adjust and optimize its behavior in response to changing conditions, enhancing its ability to learn and adapt dynamically.
  5. Responsive adaptation refers to materials that dynamically alter their structure and properties in response to external stimuli. For example, when exposed to environmental changes such as temperature, pH, or light, a gel phase material can undergo controlled swelling. This swelling triggers a transformation in the material’s internal structure, resulting in significant changes in its physical and chemical properties. Such responsive behavior enables these intelligent materials to adapt and function optimally in varying conditions, making them highly versatile for applications that require real-time, environment-sensitive adjustments.
  6. Communication Between Units or Agents: In systems with multiple intelligent materials or agents, communication enables the sharing of sensory data and learned information. This interaction allows individual units to coordinate their responses, leading to more efficient and effective collective behavior. Communication can occur through direct physical connections, wireless signals, or chemical signaling, depending on the system’s design.
  7. Artificial Swarm Intelligence has found significant applications in the field of robotics. Robot swarms consist of small, simple robots equipped with sensors, such as cameras, radar, or sonar, that allow them to gather information about their environment. These robots communicate and collaborate, forming a unified decision-making system. Swarm intelligence enables the combination of knowledge and insights from millions of independent robots to make optimized, unified decisions.
  8. Active inference involves materials or systems that use internal models to predict and respond to their environment dynamically. These systems are designed to minimize prediction errors by adjusting their behavior or structure based on sensory inputs. For instance, an intelligent material might change its properties, such as stiffness or permeability, in response to changes in temperature or pressure, effectively ‘learning’ from its surroundings. This approach allows for the development of materials that can adapt and self-regulate, enhancing their functionality and resilience in diverse applications. Active inference thus enables the creation of smart materials that can autonomously optimize their performance in real-time.

N.B. During the workshop the minimal model of intelligent soft matter will be discussed and updated.

Outcomes

Nanopublications

Promising and potentially successful ideas can be registered in form of nanopublications:

Nanopublications are a standardized, machine-readable and machine-interpretable format for publishing scientific assertions as the smallest unit of publishable information perfectly suitable for ideas recording in knowledge graphs.

They consist of three key components: an assertion (the core scientific statement of the idea), provenance (information about how the assertion was derived), authors, and publication information (metadata about the nanopublication itself for machine interconnection with other pieces of scientific knowledge). This format allows for precise attribution, easy integration and improved discoverability of scientific claims and ideas.

Opinion paper

The outcomes of the workshop are invited as a perspective article in the Soft Matter 20th anniversary themed collection

Reading

Publications on the design of future Intelligent Materials is recommended for all workshop participants:

(1) Kaspar, C.; Ravoo, B. J.; Van Der Wiel, W. G.; Wegner, S. V.; Pernice, W. H. P. The Rise of Intelligent Matter. Nature 2021, 594 (7863), 345–355. https://doi.org/10.1038/s41586-021-03453-y.
(3) McEvoy, M. A.; Correll, N. Materials That Couple Sensing, Actuation, Computation, and Communication. Science 2015, 347 (6228), 1261689. https://doi.org/10.1126/science.1261689.
(2) G. Volpe, G; et. al., Roadmap for Animate Matter, arXiv:2407.10623
https://arxiv.org/abs/2407.10623
(3) Friston, K.; Da Costa, L.; Sajid, N.; Heins, C.; Ueltzhöffer, K.; Pavliotis, G. A.; Parr, T. The Free Energy Principle Made Simpler but Not Too Simple. Physics Reports 2023, 1024, 1–29. https://doi.org/10.1016/j.physrep.2023.07.001.
(4) Manzano, G.; Kardeş, G.; Roldán, É.; Wolpert, D. H. Thermodynamics of Computations with Absolute Irreversibility, Unidirectional Transitions, and Stochastic Computation Times. Phys. Rev. X 2024, 14 (2), 021026. https://doi.org/10.1103/PhysRevX.14.021026.
(5) Rosenberg, L.; Willcox, G.; Schumann, H.; Mani, G. Conversational Swarm Intelligence Amplifies the Accuracy of Networked Groupwise Deliberations. In 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC); IEEE: Las Vegas, NV, USA, 2024; pp 0086–0091. https://doi.org/10.1109/CCWC60891.2024.10427807.
(6) Manzano, G.; Kardeş, G.; Roldán, É.; Wolpert, D. H. Thermodynamics of Computations with Absolute Irreversibility, Unidirectional Transitions, and Stochastic Computation Times. Phys. Rev. X 2024, 14 (2), 021026. https://doi.org/10.1103/PhysRevX.14.021026.