(4ge) Active Matter Coupled to Crystalline Defects Via Strain Field Optimization

Microfabrication techniques have opened the possibility to create materials that appear continuous at the human length scale, but are in fact composed of discrete, designed components. When these components are small and relatively simple such materials are known as ‘metamaterials’. The design and control of such materials for engineering purposes presents grand challenges and enormous potential, particularly when the size of each subcomponent is very small (i.e., sub-micron). When the subcomponents are capable of exerting individual forces, sensing, or communicating, a truly exotic material with potentially life-like qualities is produced. Large (i.e., centimeter) scale subunits can be robotic elements, capable of independent sensing, actuation, self-propulsion, and communication. Such collectives are typically known as modular or swarm robots.

Over the past several decades, the study of nonequilibrium model systems at the sub-micron scale (i.e., active matter) has attracted interest because of the compelling connections to living systems, as well as fundamental theoretical questions surrounding the prediction of active system dynamics. At the same time, research into multi-robot collectives has boomed. These two fields have remained largely separated; robots are macroscopic objects with complex internal states and abilities, while active colloids are microscopic objects without hidden internal states.

There is a growing need to connect these two areas of study. As we approach experimentally realized robot swarms in the metamaterial scale, communicating and coordinating action among agents becomes extremely difficult. This challenge can be addressed by understanding and controlling emergent system dynamics. Active materials, with their comparatively simple elements and inability to communicate or store internal states, still display complex group behaviors that arise without algorithmic planning. Despite the stochasticity inherent in emergent behaviors, actions can be predictable, controllable, and repeatable when the number of agents becomes large.

As a computational researcher, I have a strong interest in increasing the programming literacy of future generations of Chemical Engineers. My teaching philosophy (for computational topics as well as traditional courses) focuses on three pillars of student engagement and learning: activate and extend student prior knowledge; use inquiry-based projects to motivate concepts; and place course materials in a wider context.

Activate and extend prior knowledge. Students enter the classroom with prior knowledge, and this can help or hinder learning. When activated at the right time, and linked to instructional concepts, this prior knowledge strengthens student understanding.

Use inquiry-based projects for motivation. Content mastery requires students not only to absorb what is presented to them, but to do self-inventory and make solid connections between their prior knowledge and new concepts. Hypothesis formulation and collective problem solving in groups allow students to self-inventory while communicating with their peers.

Place course materials in a wider context. Studies have shown that students are more motivated to persevere in their studies if they understand the implications of course material for the world outside the classroom.