Materials design focuses on establishing specific structural configurations and interactions among the constituent components of a material, often at molecular scales. Once identified, the design parameters typically remain fixed to maintain a material’s properties.

Biology takes a different route, though, and generates functionality by allowing the design parameters to evolve and adapt as the environment impacts the system. For example, bone reconfigures to become stronger or more resilient at precisely those places where repeated stressing provides the cues. Our group aims to mimic biological adaptations in physical materials with the goal of creating novel functionality through the process of training.

Since the material itself carries out the adaptations, we can achieve modifications without precise redesign of the local structure. As we understand more about the trainability of materials, we expect to help materials biology and advance mastery of synthetic biological systems by identifying training mechanisms at play in the biological realm.

Defining materials training

Materials training falls under the larger umbrella of materials processing, but as defined through a link with biology, it differs in several key aspects from standard processing methods and opens a range of new opportunities.
For example, training applied at length scales much larger than individual constituent units—manipulating the material as a whole for instance—exploits intrinsic material properties to direct the training action to only those constituent parts that need to be evolved. In other words, the material itself “decides” where and how to evolve.

This mimics what happens during athletic or musical training (i.e., different training protocols to train different muscles in the body) or in the case of bone mentioned above. Taking this a step further, we envision a situation where different material properties or functions emerge simply from changing the training protocol (more on this below).

Beyond this, training can achieve “material learning,” where the material is trained to exhibit specific, desired responses to different external stimuli. We envision adaptive impact-absorbing materials that can learn to redirect internal stresses by evolving their ratio of bulk to shear modulus; shape morphing materials that learn to self-fold into different configurations; or materials that work as actuators with multiple, trainable functionalities by learning different spatial or temporal patterns of applied stress and responding with pattern-specific outcomes.

In all these cases, retraining using a different protocol of applying mechanical stimuli might be used to modify the material to generate a new response. This highlights a difference with standard materials processing methods, which condition a material for a single purpose.

The suitability of soft materials

Soft materials have a multitude of easily accessible, energetically similar configurations that make them particularly well-suited for training to amplify a desired property.

In soft materials, training protocols based on applying mechanical stress provide straightforward access to nonlinear regimes and allow for the activation of chemical pathways. Both of these help to imprint a memory by triggering long-lasting changes, such as plastic structural deformation.

Many materials and patterns can be profitably modeled as networks. Macroscopic mechanical metamaterials composed of nodes and struts, crosslinked polymers and biological fibers, the above-mentioned bone, and even the creases in folded sheets all have a network structure where links between nodes can be clearly identified. We can then think of training as evolving the properties of the links and/or the configuration of the nodes.

Viewing soft materials as prototypical adaptive materials, our group focuses on networks for achieving our research goals. We address specific types of soft matter networks that share similar characteristics in the three focus areas (FAs) presented below.

Potential of other materials

Clearly, not all materials can be trained toward a useful outcome, and proper choice of a training protocol will be required to reach a desired material performance. A key goal of our group is to understand the requirements for a material to be “trainable/retrainable” and the possibilities and limits of training.

Trainability, together with learning and memory, forms a key element in the evolution of biological systems and, more recently, in machine learning with computers. Aspects of training have also been used in materials processing—to harden metal, align polymer domains, or activate current paths in neuromorphic computation devices. However, no systematic framework currently exists for designing trainable soft materials and establishing their training protocols.

We aim to develop such a framework by taking advantage of an emerging synergy among recent ideas from biology, materials science, polymer chemistry, and soft matter physics, all of which address different aspects of learning and memory in complex systems.

We build on pioneering initial research at UChicago MRSEC, including a recent iSuperSeed2 effort aligned with the National Science Foundation initiative “Understanding the Rules of Life.” This iSuperSeed2 indicated the exciting potential of a training-based approach for creating multifunctional, pluripotent materials. At the same time, it showed that insights gained can aid the understanding of biological systems in revealing principles of adaptability.

Areas of expertise

The new types of trainable materials we envision can exist from the macroscopic to the molecular scale. In their most potent manifestations, they will require careful consideration of all these scales.

To achieve this vision, our group brings together expertise in polymer synthesis/characterization (Esser-Kahn, Rowan), nano-to-macro experiments (Driscoll, Jaeger, Nagel, Tian), soft-materials modeling (Olvera de la Cruz, Witten), bio-soft matter theory (Dinner, Murugan), and learning in neural networks (Palmer).

FAs within the group explore different aspects of training in three classes of soft matter. FA1 explores macroscopic network-based materials, FA2 dynamic polymer networks, and FA3 particle/gel-network composites. FA1 focuses on training by exploiting adaptivity of the links, while in FA2 and FA3 node configuration can also change during training.

All three FAs address similar key questions concerning trainability but explore them within the context of specific material systems. We ask how training can select specific, targeted behaviors or properties from many possible outcomes, how various input-specific responses can be learned, and how suitable training protocols can be identified. We aim to investigate the scope, limits, and benefits of training as a new paradigm for generating novel multifunctionality in soft materials.