Tyler O'Neal, Staff Editor CHEMISTRY

Better supercomputer models of atmospheric detergent can help predict climate change

New research from Rochester scientist Lee Murray will aid in building more accurate computer models of the hydroxyl radical (OH), an important ‘detergent of the atmosphere.’

Earth’s atmosphere has a unique ability to cleanse itself by way of invisible molecules in the air that act as minuscule cleanup crews. The most important molecule in that crew is the hydroxyl radical (OH), nicknamed the “detergent of the atmosphere” because of its dominant role in removing pollutants. When the OH molecule chemically interacts with a variety of harmful gases, including the potent greenhouse gas methane, it is able to decompose the pollutants into forms that can be removed from Earth’s atmosphere.

It is difficult to measure OH, however, and it is not directly emitted. Instead, researchers predict the presence of OH based on its chemical production from other, “precursor” gases. To make these predictions, researchers use supercomputer simulations. In the Earth’s atmosphere, hydroxyl radical (OH) plays a dominant role in removing pollutants—but the OH molecule is difficult to measure. New research from Rochester scientist Lee Murray and his colleagues explains why the supercomputer models used to predict future levels of OH have traditionally produced widely varying forecasts. (Getty Images)

Lee Murray, an assistant professor of earth and environmental sciences at the University of Rochester, outlines why supercomputer models used to predict future levels of OH—and, therefore, how long air pollutants and reactive greenhouse gases last in the atmosphere—have traditionally produced widely varying forecasts. The study is the latest in Murray’s efforts to develop models of the dynamics and composition of Earth’s atmosphere and has important implications in advancing policies to combat climate change.

“We need to understand what controls changes in hydroxyl radical in Earth’s atmosphere in order to give us a better idea of the measures we need to take to rid the atmosphere of pollutants and reactive greenhouse gases,” Murray says.

Building accurate computer models to predict OH levels is similar to baking: just as you must add precise ingredients in the proper amounts and order to make an edible cake, precise data and metrics must be input into supercomputer models to make them more accurate.

The various existing supercomputer models used to predict OH levels have traditionally been designed with data input involving identical emissions levels of OH precursor gases. Murray and his colleagues, however, demonstrated that OH levels strongly depend on how much of these precursor emissions are lost before they react to produce OH. In this case, different bakers follow the same recipe of ingredients (emissions), but end up with different sizes of cake (OH levels) because some bakers throw out different portions of batter in the middle of the process.

“Uncertainties in future predictions are primarily driven by uncertainties in how models implement the fate of reactive gases that are directly emitted,” Murray says.

As Murray and his colleagues show, the computer models used to predict OH levels must evaluate the loss processes of reactive precursor gases, before they may be used for accurate future predictions.

But more data is needed about these processes, Murray says.

“Performing new measurements to constrain these processes will allow us to provide more accurate data about the amount of hydroxyl in the atmosphere and how it may change in the future,” he says.

RIKEN theoretical physicists use machine learning to understand finite-temperature quantum effects better

Tyler O'Neal, Staff Editor CHEMISTRY

Machine learning can help explore the interaction between thermal and quantum effects in many-body systems Three RIKEN theoretical physicists have used an artificial neural network, which mimics the way neurons are connected in the brain, to investigate the temperature evolution of quantum many-body systems. © KTSDESIGN/SCIENCEPHOTOLIBRARY

Three RIKEN theoretical physicists have used neural networks to investigate the way atoms and electrons interact with each other at finite temperatures. This knowledge will help inform the development of future quantum technologies for advanced computation.

Many of a material’s properties, both conventional and exotic, originate from atoms and electrons interacting with each other according to the laws of quantum mechanics. Understanding these so-called quantum many-body systems is critical for predicting and controlling these properties. In addition, this knowledge will be vital for developing practically useful devices such as quantum computers.

A large number of interactions makes modeling quantum many-body systems challenging even for temperatures near absolute zero, but this becomes much harder as the temperature rises. Numerical methods that can account for the nontrivial interplay between thermal and quantum fluctuations require prohibitively high computational costs, often becoming intractable even by the most powerful supercomputers in the world.

“The numerical complexity of treating quantum many-body systems means that there is a dearth of powerful methods for finite-temperature simulations,” says Yusuke Nomura from the RIKEN Center for Emergent Matter Science. “To overcome this difficulty, we have developed several efficient methods that employ machine learning.”

Nomura, together with RIKEN colleagues Nobuyuki Yoshioka and Franco Nori, has now developed two mathematical techniques that use neural networks to model thermal effects in quantum many-body systems.

A neural network is an interconnected array of nodes that are designed to process information in a way that mimics neurons in the brain. Neural networks have found applications in machine learning and artificial intelligence. “The flexibility of artificial neural networks allowed us to construct compact and accurate expressions of many-body quantum states in thermal equilibrium,” explains Nomura.

The first of the cutting-edge approaches taken by the trio was to use a machine-learning process known as a deep Boltzmann machine to create a mathematical description of a quantum many-body system called the Gibbs state. Their second method employed so-called stochastic sampling to optimize the parameters of their network.

“The ultimate goal of our approach is to reveal complex finite-temperature phenomena that remain unexplored in a wide range of fields, including condensed-matter physics, atomic physics, statistical mechanics, and quantum optics,” says Nomura. “While we need to improve the method, we’re confident it will give us a better understanding of the thermal behavior of quantum many-body systems, which in turn will provide a stronger foundation for designing future quantum devices and investigating new functional materials.”

Georgia Tech researchers discover predictable behavior in promising material for memory

Tyler O'Neal, Staff Editor CHEMISTRY

A research team led by Georgia Tech researchers has discovered unexpectedly familiar behavior in the antiferroelectric material known as zirconium dioxide or zirconia. Research findings recently featured on the cover of the journal Advanced Electronic Materials.

In the last few years, a class of materials called antiferroelectrics has been increasingly studied for its potential applications in modern computer memory devices. Research has shown that antiferroelectric-based memories might have greater energy efficiency and faster read and write speeds than conventional memories, among other appealing attributes. Further, the same compounds that can exhibit antiferroelectric behavior are already integrated into existing semiconductor chip manufacturing processes.

Now, a team led by Georgia Tech researchers has discovered unexpectedly familiar behavior in the antiferroelectric material known as zirconium dioxide or zirconia. They show that as the microstructure of the material is reduced in size, it behaves similarly to much better-understood materials known as ferroelectrics. The findings were recently published in the journal Advanced Electronic Materials.

Miniaturization of circuits has played a key role in improving memory performance over the last fifty years. Knowing how the properties of an antiferroelectric change with shrinking size should enable the design of more effective memory components.

The researchers also note that the findings should have implications in many other areas besides memory.

"Antiferroelectrics have a range of unique properties like high reliability, high voltage endurance, and broad operating temperatures that makes them useful in a wealth of different devices, including high-energy-density capacitors, transducers, and electro-optics circuits,” said Nazanin Bassiri-Gharb, coauthor of the paper and professor in the Woodruff School of Mechanical Engineering and the School of Materials Science and Engineering at Georgia Tech. “But size scaling effects had gone largely under the radar for a long time.”

“You can design your device and make it smaller knowing exactly how the material is going to perform,” said Asif Khan, co-author of the paper and assistant professor in the School of Electrical and Computer Engineering and the School of Materials Science and Engineering at Georgia Tech. “From our standpoint, it opens a new field of research.”

Lasting Fields

The defining feature of an antiferroelectric material is the peculiar way it responds to an external electric field. This response combines features of non-ferroelectric and ferroelectric materials, which have been much more intensively studied in physics and materials science.

For ferroelectrics, exposure to an external electric field of sufficient strength makes the material become strongly polarized, which is a state where the material exhibits its internal electric field. Even when the external electric field is removed, this polarization persists, similar to how an iron nail can become permanently magnetized.

The behavior of a ferroelectric material also depends on its size. As a sample of material is made thinner, a stronger electric field is required to create a permanent polarization, by a precise and predictable law called the Janovec–Kay–Dunn (JKD) law.

By contrast, the application of an external electric field to an antiferroelectric does not cause the material to become polarized – at first. However, as the strength of the external field is increased, an antiferroelectric material eventually switches to a ferroelectric phase, where polarization abruptly sets in. The electric field needed to switch the antiferroelectric to a ferroelectric phase is called the critical field.  Nazanin Bassiri-Gharb, Harris Saunders Jr. Chair and Professor, the Woodruff School of Mechanical Engineering and the School of Materials Science and Engineering at Georgia Tech

Size Scaling

In the new work, the researchers discovered that zirconia antiferroelectrics also obeys something like a JKD law. However, unlike ferroelectrics, the microstructure of the material plays a key role. The strength of the critical field scales in the JKD pattern specifically for the size of structures known as crystallites within the material. For a smaller crystallite size, it takes a stronger critical field to switch an antiferroelectric material into its ferroelectric phase, even if the thinness of the sample remains the same.

“There had not been a predictive law that dictates how the switching voltage will change as one miniaturizes these antiferroelectric oxide devices,” said Khan. “We’ve found a new twist on an old law.”

Formerly, thin antiferroelectrics had been difficult to produce incomparable sizes as ferroelectrics, the researchers said. Nujhat Tasneem, the doctoral student leading the research, spent “day and night” in the lab according to Khan to process and produce leakage-free antiferroelectric zirconium oxide films of single nanometers in size. The next step, according to Khan, is for researchers to figure out exactly how to control the crystallite size, thereby tailoring the properties of the material for its use in circuits.

The researcher also collaborated with researchers from the Charles University in the Czech Republic and the Universidad Andres Bello in Chile for X-ray diffraction characterization and first-principles-based calculations, respectively.

“It was truly a collaborative effort, spanning multiple continents,” said Tasneem.

The results should also speak to fundamental physics questions, according to Bassiri-Gharb. In recent years, something of a mystery has arisen in the study of antiferroelectrics, with the way that microscopic crystalline structures cause a macroscopic polarization being called into question.

“Finding two very different types of materials – ferroelectric and antiferroelectrics with different atomic structures – to follow similar behaviors and laws is particularly exciting,” said Bassiri-Gharb. “It opens doors for searching for more similarities and transferring more of our knowledge across the fields.”