Seeing the music in nature

If anyone were going to discover the connections between molecular structures, mathematical concepts and musical scores, it’s not surprising that Markus Buehler would be the one. He has built his career on bridging the connections between disparate disciplines, asking simple questions as an approach to understanding the world.

Buehler grew up around engineering: His father is a mechanical engineer, his mother worked in the automotive industry and his two brothers became engineers as well (one mechanical, one computer). During his youth, Buehler was drawn to understanding how things worked, and started designing and building electronic circuits when he was about 11. Soon after, he began writing programs to regulate common things around his house, such as the household’s solar-panel system for heating water and his electric train setup, automating the switching of tracks. Later, he wrote programs that simulated the dynamics of stock trading.

In addition to building things, Buehler says, “I enjoyed breaking things apart to learn how they were made, and using what I had learned to build it again with a new approach or with improvements. I built a radio and an intercom system for our house by taking apart telephones. It took a couple of iterations, but I finally got it to work well.”

On the side, Buehler taught himself how to play several instruments and began composing his own pieces. Though he no longer finds time to compose music, he enjoys sharing his musical interests with his children, including teaching them to play the guitar. His passion for music has also come into play recently in his research, which found significant correlations between the structure of spider webs and principles of musical composition.

Simulating materials

During high school, Buehler was fascinated by chemistry and intrigued by how materials derive their properties from the structure of their molecules. After earning his PhD at the Max Planck Institute in Stuttgart, Germany, he went to the California Institute of Technology as a postdoc. That’s where he began working on computer simulations of chemically complex materials, and began to incorporate such models in the description of biological materials — a specialty that has remained a cornerstone of his research ever since.

Photo: M. Scott Brauer

“I changed fields completely, but only in terms of the application of my work,” he says. Having done his doctoral research in materials science working on metals, he decided to focus on understanding features of natural phenomena, studying biological materials such as bone, spider webs or the proteins that make up the structures in our body’s cells.

“I saw an opportunity to combine a chemistry-based approach with molecular simulations and multiscale modeling,” Buehler says. Such approaches can be used not only to understand the function of biological materials in their physiological context, but also to appreciate the breakdown of diseased tissues.

Between nature and engineering

Given his focus on biological materials, people often ask Buehler how he ended up on the faculty of MIT’s Department of Civil and Environmental Engineering — a field more traditionally associated with concrete and steel on the one hand, and oceans and ecosystems on the other. “My work falls in between these fields, at the interface between the natural and the engineering-built world” he says. “I saw an opportunity to work on projects that could be in useful for both sides. It’s a unique combination to work at this interface, and a good fit for what I was interested in.” He was awarded tenure last fall.

Since joining the MIT faculty in 2006, Buehler has focused on understanding biological materials such as spider silk and the tangled masses of protein known as amyloids — primarily as a way to understand how their complex structures could improve the functional properties of manmade materials. He has also collaborated with his wife, whose experimental research at Harvard University focused on the interactions of cells with materials. “She has taught me a lot about biology, and how simulations might contribute to the field,” he says.

But “our focus is not to just copy nature” or the kinds of materials nature produces, Buehler says. Rather, he’d like to learn the underlying principles of how complex, hierarchical structures with useful properties can be assembled from the simplest of building blocks — and how engineers can actually apply such knowledge in different materials or in different problems altogether.

“Here, I feel like I have an opportunity to do something new,” Buehler says. “At MIT, we don’t believe in keeping things the same, we continue to push the boundaries of innovation.” Civil and environmental engineering research “is no longer just about building bridges, it’s about using nanotechnology and scalability to improve the materials we use to build and maintain our infrastructure, and to improve the interface between the natural and built world from the tiny atoms to the tallest of structures. It’s exciting to be part of redefining this field.”

MIT Video – Rocks, Bands, Logic

MIT Video – Rocks, Bands, Logic.

Self-sculpting sand New algorithms could enable heaps of ‘smart sand’ that can assume any shape, allowing spontaneous formation of new tools or duplication of broken mechanical parts.

Imagine that you have a big box of sand in which you bury a tiny model of a footstool. A few seconds later, you reach into the box and pull out a full-size footstool: The sand has assembled itself into a large-scale replica of the model.

That may sound like a scene from a Harry Potter novel, but it’s the vision animating a research project at the Distributed Robotics Laboratory (DRL) at MIT’s Computer Science and Artificial Intelligence Laboratory. At the IEEE International Conference on Robotics and Automation in May — the world’s premier robotics conference — DRL researchers will present a paper describing algorithms that could enable such “smart sand.” They also describe experiments in which they tested the algorithms on somewhat larger particles — cubes about 10 millimeters to an edge, with rudimentary microprocessors inside and very unusual magnets on four of their sides.

Unlike many other approaches to reconfigurable robots, smart sand uses a subtractive method, akin to stone carving, rather than an additive method, akin to snapping LEGO blocks together. A heap of smart sand would be analogous to the rough block of stone that a sculptor begins with. The individual grains would pass messages back and forth and selectively attach to each other to form a three-dimensional object; the grains not necessary to build that object would simply fall away. When the object had served its purpose, it would be returned to the heap. Its constituent grains would detach from each other, becoming free to participate in the formation of a new shape.

Distributed intelligence

Algorithmically, the main challenge in developing smart sand is that the individual grains would have very few computational resources. “How do you develop efficient algorithms that do not waste any information at the level of communication and at the level of storage?” asks Daniela Rus, a professor of computer science and engineering at MIT and a co-author on the new paper, together with her student Kyle Gilpin. If every grain could simply store a digital map of the object to be assembled, “then I can come up with an algorithm in a very easy way,” Rus says. “But we would like to solve the problem without that requirement, because that requirement is simply unrealistic when you’re talking about modules at this scale.” Furthermore, Rus says, from one run to the next, the grains in the heap will be jumbled together in a completely different way. “We’d like to not have to know ahead of time what our block looks like,” Rus says.

To attach to each other, to communicate and to share power, the cubes use ‘electropermanent magnets,’ materials whose magnetism can be switched on and off with jolts of electricity. Each cube has magnets — recognizable by the reddish wires wrapped around them — on four of its six faces.
Photo: M. Scott Brauer

Conveying shape information to the heap with a simple physical model — such as the tiny footstool — helps address both of these problems. To get a sense of how the researchers’ algorithm works, it’s probably easiest to consider the two-dimensional case. Picture each grain of sand as a square in a two-dimensional grid. Now imagine that some of the squares — say, in the shape of a footstool— are missing. That’s where the physical model is embedded.

According to Gilpin-author on the new paper, the grains first pass messages to each other to determine which have missing neighbors. (In the grid model, each square could have eight neighbors.) Grains with missing neighbors are in one of two places: the perimeter of the heap or the perimeter of the embedded shape.

Once the grains surrounding the embedded shape identify themselves, they simply pass messages to other grains a fixed distance away, which in turn identify themselves as defining the perimeter of the duplicate. If the duplicate is supposed to be 10 times the size of the original, each square surrounding the embedded shape will map to 10 squares of the duplicate’s perimeter. Once the perimeter of the duplicate is established, the grains outside it can disconnect from their neighbors.

Rapid prototyping

The same algorithm can be varied to produce multiple, similarly sized copies of a sample shape, or to produce a single, large copy of a large object. “Say the tire rod in your car has sheared,” Gilpin says. “You could duct tape it back together, put it into your system and get a new one.”

The cubes — or “smart pebbles” — that Gilpin and Rus built to test their algorithm enact the simplified, two-dimensional version of the system. Four faces of each cube are studded with so-called electropermanent magnets, materials that can be magnetized or demagnetized with a single electric pulse. Unlike permanent magnets, they can be turned on and off; unlike electromagnets, they don’t require a constant current to maintain their magnetism. The pebbles use the magnets not only to connect to each other but also to communicate and to share power. Each pebble also has a tiny microprocessor, which can store just 32 kilobytes of program code and has only two kilobytes of working memory.

The pebbles have magnets on only four faces, Gilpin explains, because, with the addition of the microprocessor and circuitry to regulate power, “there just wasn’t room for two more magnets.” But Gilpin and Rus performed computer simulations showing that their algorithm would work with a three-dimensional block of cubes, too, by treating each layer of the block as its own two-dimensional grid. The cubes discarded from the final shape would simply disconnect from the cubes above and below them as well as those next to them.

True smart sand, of course, would require grains much smaller than 10-millimeter cubes. But according to Robert Wood, an associate professor of electrical engineering at Harvard University, that’s not an insurmountable obstacle. “Take the core functionalities of their pebbles,” says Wood, who directs Harvard’s Microrobotics Laboratory. “They have the ability to latch onto their neighbors; they have the ability to talk to their neighbors; they have the ability to do some computation. Those are all things that are certainly feasible to think about doing in smaller packages.”

“It would take quite a lot of engineering to do that, of course,” Wood cautions. “That’s a well-posed but very difficult set of engineering challenges that they could continue to address in the future.”