Building a better mouse trap, from the atoms up

08-Mar-2016 - USA

For most of human history, the discovery of new materials has been a crapshoot. But now, UConn researchers have systematized the search with machine learning that can scan millions of theoretical compounds for qualities that would make better solar cells, fibers, and computer chips. The search for new materials may never be the same.

No one knows why an early metallurgist decided to smelt a hunk of tin into some copper, but the resulting bronze alloy was harder and more durable than any material previously known. Most materials experimentation over the ensuing 7,000 years has been similarly random, guided largely by philosophy and chemical intuition.

But in a world that contains at least 95 stable elements - the basic building blocks of matter - the number of possible combinations is enormous, and experimentation is an awfully inefficient way to find what you're looking for.

Lefty or righty molecules lend a hand to material structures

Effort to build bottom-up chiral polymers with unique functions

Read news

Enter UConn materials scientist Ramamurthy 'Rampi' Ramprasad. Instead of randomly mixing chemicals to see what they do, Ramprasad designs them rationally, using machine learning to figure out which atomic configurations make a polymer a good electrical conductor or insulator.

A polymer is a large molecule made of many repeating building blocks. Polymers are very common in both living and man-made materials. Probably the most familiar example is plastics, and the wide variation in plastics - which can be hard, soft, stretchy, brittle, spongy, clear, opaque or translucent - gives an inkling of how diverse polymers in general can be.

Polymers can also have diverse electronic properties. For example, they can be very good insulators - preventing electrons, and thus electric current, from traveling through them - or good conductors, allowing electricity to pass through them freely. And what controls all these properties is mainly how the atoms in the polymer connect to each other. But until recently, no one had systematically related properties to atomic configurations.

So Ramprasad and his colleagues decided to do just that. First, they would analyze known polymers, using laborious but accurate quantum mechanics-based calculations to figure out which arrangements of atoms confer which properties, and quantify those atomic-level relationships via a string of numbers that fingerprint each polymer. Once they had those, they could have a computer search through any number of theoretical polymers to figure out which ones might have which properties. Then anyone looking for a polymer with a certain property could quickly scan the list and decide which theoretical polymers might be worth trying.

Many polymers are made of building blocks containing just a few atoms. For instance, polyurea, a common plastic, has as the basic structure a repeating sequence of nitrogen, hydrogen and oxygen: NH-O-NH-O. Most polymers look like that, made of carbon, H, N and O, with a few other elements thrown in occasionally.

For their project, Ramprasad's group looked at polymers made of just seven building blocks: CH₂, C₆H₄, CO, O, NH, CS, and C₄H₂S. These are found in common plastics such as polyethylene, polyesters, and polyureas. An enormous variety of polymers could theoretically be constructed using just these building blocks; Ramprasad's group decided at first to analyze just 283, each composed of a repeated four-block unit.

They started from basic quantum mechanics, and calculated the three-dimensional atomic and electronic structures of each of those 283 four-block polymers. This is not trivial: calculating the position of every electron and atom in a molecule with more than two atoms takes a powerful computer a significant chunk of time, which is why they did it for only 283 molecules.

Once they had the three-dimensional structures, they could calculate what they really wanted to know: each polymer's properties. They calculated the band gap, which is the amount of energy it takes for an electron in the polymer to break free of its home atom and travel around the material, and the dielectric constant, which is a measure of the effect an electric field can have on the polymer. These properties translate to how much electric energy each polymer can store in itself. The researchers used established techniques that have long been known. They take a prohibitive amount of computing time, which is why it's so hard to evaluate materials this way.

Ramprasad's group then went one step further. They wanted a shorthand system that a computer could use to look at the building blocks of a polymer and how they connect to each other, and make educated guesses about its properties.

Computers deal with numbers, so first they had to define each polymer as a string of numbers, a sort of numerical fingerprint. Since there are seven possible building blocks, there are seven possible numbers, each indicating how many of each block type are contained in that polymer. But a simple number string like that doesn't give enough information about the polymer's structure, so they added a second string of numbers that tell how many pairs there are of each combination of building blocks, such as NH-O or C₆H₄-CS. Still not quite enough information, so they added a third string that described how many triples, like NH-O-CH₂, there were. They arranged these strings as a three-dimensional matrix, which is a convenient way to describe such strings of numbers in a computer.

Then they let the computer go to work. Using the library of 283 polymers they had laboriously calculated using quantum mechanics, the machine compared each polymer's numerical fingerprint to its band gap and dielectric constant, and gradually 'learned' which building block combinations were associated with which properties. It could even map those properties onto a two-dimensional matrix of the polymer building blocks.

Once the machine learned which atomic building block combinations gave which properties, it no longer needed the quantum mechanics calculations of atomic structure. It could accurately evaluate the band gap and dielectric constant for any polymer made of any combination of those seven building blocks, using just the numerical fingerprint of its structure.

Original publication

Arun Mannodi-Kanakkithodi, Ghanshyam Pilania, Tran Doan Huan, Turab Lookman & Rampi Ramprasad; "Machine Learning Strategy for Accelerated Design of Polymer Dielectrics"; Scientific Reports; 2016

https://www.chemeurope.com/en/news/157177/building-a-better-mouse-trap-from-the-atoms-up.html

Original publication

Arun Mannodi-Kanakkithodi, Ghanshyam Pilania, Tran Doan Huan, Turab Lookman & Rampi Ramprasad; "Machine Learning Strategy for Accelerated Design of Polymer Dielectrics"; Scientific Reports; 2016

Topics

polymers building blocks perovskites polyethylene engineering

Show all

Organizations

University of Connecticut