Nav: Home

Scaling forward

March 22, 2019

Argonne scientist's approach to molecular modeling may accelerate the development of new organic materials for electronics.

Organic electronics have the potential to revolutionize technology with their high cost-efficiency and versatility compared with more commonly used inorganic electronics. For example, their flexibility could allow companies to print them like paper or incorporate them into clothing to power wearable electronics. However, they have failed to gain much industry traction due to the difficulty of controlling their electronic structure.

To help address this challenge, Nick Jackson, a Maria Goeppert Mayer Fellow at the U.S. Department of Energy's (DOE) Argonne National Laboratory, has developed a faster way of creating molecular models by using machine learning. Jackson's models dramatically accelerate the screening of potential new organic materials for electronics and they could also be useful in other areas of materials science research.

"It's kind of like a game of Tetris," -- Nick Jackson, Maria Goeppert Mayer Fellow at Argonne

The internal structure of an organic material affects its electrical efficiency. The current manufacturing processes used to produce these materials are sensitive, and the structures are extremely complex. This makes it difficult for scientists to predict the final structure and efficiency of the material based on manufacturing conditions. Jackson uses machine learning, a way of training a computer to learn a pattern without being explicitly programmed, to help make these predictions. 

Jackson's research focuses on vapor deposition as a means to assemble materials for organic electronics. In this process, scientists evaporate an organic molecule and allow it to slowly condense on a surface, producing a film. By manipulating certain deposition conditions, the scientists can finely tune the way the molecules pack in the film.

"It's kind of like a game of Tetris," said Jackson. "The molecules can orient themselves in different ways, and our research aims to determine how that structure influences the electronic properties of the material."

The packing of the molecules in the film affects the material's charge mobility, a measure of how easily charges can move inside it. The charge mobility plays a role in the efficiency of the material as a device. To study this relationship, and to optimize device performance, Jackson's team runs extremely detailed computer simulations of the vapor deposition process.

"We have models that simulate the behavior of all of the electrons around each molecule at nanoscopic length and time scales," said Jackson, "but these models are computationally intensive, and therefore take a very long time to run."

To simulate the packing of entire devices, often containing millions of molecules, scientists must develop computationally cheaper, coarser models that describe the behavior of electrons in groups of molecules rather than individually. These coarse models can reduce computation time from hours to minutes, but the challenge is in making the coarse models truly predictive of the physical results. Jackson uses his machine learning algorithms to uncover the relationships between the detailed and coarse models.

"I drop my hands and leave it to machine learning to regress the relationship between the coarse description and the resulting electronic properties of my system," Jackson said.

Using an artificial neural network and a learning process called backpropagation, the machine learning algorithm learns to extrapolate from coarse to more detailed models. Using the complex relationship that it finds between the models, it trains itself to predict the same electronic properties of the material using the coarse model as the detailed model would predict.

"We are developing cheaper models that still reproduce all of the expensive properties," said Jackson.

The resulting coarse model allows the scientists to screen two to three orders of magnitude more packing arrangements than before. The results of the analysis from the coarse model then help experimentalists to more quickly develop high-performance materials.

Shortly after Jackson began his appointment under University of Chicago professor and Argonne Senior Scientist Juan de Pablo, he had the idea to accelerate his research with machine learning. He then took advantage of the laboratory's high-performance computing capabilities by collaborating with Venkatram Vishwanath, Data Sciences and Workflows Team Lead with the Argonne Leadership Computing Facility, a DOE Office of Science User Facility.

Materials scientists have used machine learning before to find relationships between molecular structure and device performance, but Jackson's approach is unique, as it aims to do this by enhancing the interaction between models of different length and time scales.

"In the physics community, researchers try to understand the properties of a system from a coarser perspective and to reduce the number of degrees of freedom to simplify it as much as possible," said Jackson.

Although the targeted goal of this research is to screen vapor deposited organic electronics, it has potential application in many kinds of polymer research, and even fields such as protein science. "Anything where you are trying to interpolate between a fine and coarse model," he added.

In addition to its broader applications, Jackson's advancements will help propel organic electronics towards industrial relevance.
-end-
A paper describing Jackson's approach, titled "Electronic Structure at Coarse-Grained Resolutions from Supervised Machine Learning," was published on March 22 in Science Advances.

This research was funded by Argonne's Laboratory Directed Research and Development (LDRD) program and DOE Office of Science, Basic Energy Sciences Program.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America's scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy's Office of Science.

The U.S. Department of Energy's Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.

DOE/Argonne National Laboratory

Related Molecules Articles:

Water molecules are gold for nanocatalysis
Nanocatalysts made of gold nanoparticles dispersed on metal oxides are very promising for the industrial, selective oxidation of compounds, including alcohols, into valuable chemicals.
Water molecules dance in three
An international team of scientists has been able to shed new light on the properties of water at the molecular level.
How molecules self-assemble into superstructures
Most technical functional units are built bit by bit according to a well-designed construction plan.
Breaking down stubborn molecules
Seawater is more than just saltwater. The ocean is a veritable soup of chemicals.
Shaping the rings of molecules
Canadian chemists discover a natural process to control the shape of 'macrocycles,' molecules of large rings of atoms, for use in pharmaceuticals and electronics.
The mysterious movement of water molecules
Water is all around us and essential for life. Nevertheless, research into its behaviour at the atomic level -- above all how it interacts with surfaces -- is thin on the ground.
Spectroscopy: A fine sense for molecules
Scientists at the Laboratory for Attosecond Physics have developed a unique laser technology for the analysis of the molecular composition of biological samples.
Looking at the good vibes of molecules
Label-free dynamic detection of biomolecules is a major challenge in live-cell microscopy.
Colliding molecules and antiparticles
A study by Marcos Barp and Felipe Arretche from Brazil published in EPJ D shows a model of the interaction between positrons and simple molecules that is in good agreement with experimental results.
Discovery of periodic tables for molecules
Scientists at Tokyo Institute of Technology (Tokyo Tech) develop tables similar to the periodic table of elements but for molecules.
More Molecules News and Molecules Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Listen Again: The Power Of Spaces
How do spaces shape the human experience? In what ways do our rooms, homes, and buildings give us meaning and purpose? This hour, TED speakers explore the power of the spaces we make and inhabit. Guests include architect Michael Murphy, musician David Byrne, artist Es Devlin, and architect Siamak Hariri.
Now Playing: Science for the People

#576 Science Communication in Creative Places
When you think of science communication, you might think of TED talks or museum talks or video talks, or... people giving lectures. It's a lot of people talking. But there's more to sci comm than that. This week host Bethany Brookshire talks to three people who have looked at science communication in places you might not expect it. We'll speak with Mauna Dasari, a graduate student at Notre Dame, about making mammals into a March Madness match. We'll talk with Sarah Garner, director of the Pathologists Assistant Program at Tulane University School of Medicine, who takes pathology instruction out of...
Now Playing: Radiolab

What If?
There's plenty of speculation about what Donald Trump might do in the wake of the election. Would he dispute the results if he loses? Would he simply refuse to leave office, or even try to use the military to maintain control? Last summer, Rosa Brooks got together a team of experts and political operatives from both sides of the aisle to ask a slightly different question. Rather than arguing about whether he'd do those things, they dug into what exactly would happen if he did. Part war game part choose your own adventure, Rosa's Transition Integrity Project doesn't give us any predictions, and it isn't a referendum on Trump. Instead, it's a deeply illuminating stress test on our laws, our institutions, and on the commitment to democracy written into the constitution. This episode was reported by Bethel Habte, with help from Tracie Hunte, and produced by Bethel Habte. Jeremy Bloom provided original music. Support Radiolab by becoming a member today at Radiolab.org/donate.     You can read The Transition Integrity Project's report here.