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Researchers Andrés Montoya-Castillo and Julia Moriarty are named U.S. Department of Energy Early Career Researchers, receiving multiyear funding

Two ���񱦵� researchers have been selected as U.S. Department of Energy Early Career Research Program scientists, a designation intended to support the next generation of U.S. STEM leaders.

Andrés Montoya-Castillo, an assistant professor in the Department of Chemistry, and Julia Moriarty, an assistant professor in the Department of Atmospheric and Oceanic Sciences and a fellow in the Institute of Arctic and Alpine Research, are among from across the United States whose research spans astrophysics and artificial intelligence to fusion-energy and quantum materials. The 93 scientists will share in $135 million in research funding for projects of up to five years.

“Supporting America’s scientists and researchers early in their careers will ensure the United States remains at the forefront of scientific discovery,” U.S. Secretary of Energy Jennifer M. Granholm states in the awards announcement. “The funding … gives the recipients the resources to find the answers to some of the most complex questions as they establish themselves as experts in their fields.” 

Understanding how molecules dance

Montoya-Castillo’s research is guided, in part, by the need to know which molecules are “going to be good candidates for some technological adventure,” he says. “We need to know how that molecule interacts with light.”

Researcher Andrés Montoya-Castillo studies molecular movement to better understand how they absorb energy.

One of the biggest challenges to understanding molecules is the fact that they don’t stop moving. Far from the static picture on a textbook page, molecules “are always dancing, always jiggling about,” Montoya-Castillo says. “When they jiggle about, sometimes photons or little particles of light that they wouldn’t have been able to absorb, now they can. Or the opposite could be true: They can’t absorb particles we thought they could, because they’re jiggling about, or can’t do it as well.”

Knowing how molecules in liquids and solids absorb light has the potential to support the development of everything from more efficient solar cells to organic semiconductors and biological dyes. But knowing molecules means knowing how they dance, a longtime roadblock in designing materials that maximize energy conversion, say, or enhance quantum computing.

So, Montoya-Castillo and his research group will attack this problem with statistics. “One of deepest aspects of theoretical chemistry is saying, ‘OK, we have a random-looking process. What kind of statistics does this random process follow?” he says. “We’re looking to bridge the randomness to establish a fully predictive simulation.”

The researchers will initially apply their techniques to porphyrins, which are molecules prevalent everywhere on Earth and involved in everything from oxygen transport to energy transfer; they cause the red in blood and the green in plants. Montoya-Castillo notes that porphyrins are ideal for testing the techniques because they are highly tunable and are critical ingredients in natural and artificial energy conversion.

“One of the questions we’re asking is, ‘How do we arrive at design principles to make the next generation of photo catalysts or energy conversion devices, the next generation of quantum computing or quantum sensing?’” he says.

“To do this, we need to achieve two things. The first is realize when our wonderful theories and models are not sufficient to predict and explain the physics that one gets from experiment and generalize our approach. We are doing that by developing the theoretical framework required to predict the spectra of molecules whose constant jiggling makes it difficult to know when they will absorb photons.

“The second is to exploit the current models when they work to give us insight. And fast. To tackle this second challenge, we’re working on being able to exploit experimental data to parameterize the model automatically and use this as a starting point to predict how molecules interact with light. Then we’ll be able to match our predictions to experiment, refine the model and our understanding, and speed up feedback loop of theory-experiment-design, which has traditionally been a very computationally complex and expensive procedure.

He adds that, “One of the final things we’re doing is developing a machine-learning framework to reduce this huge computational cost so we can really accelerate the pathway to tweaking these molecules to get some technological advances going for us.”

Climate change and coastal flooding

For Moriarty, a coast oceanographer by training, the path to her DOE-supported research began with a practical observation: As storms become slower and wetter because of climate change, they are dumping a lot more rain on coastal areas. Couple that with sea level rise caused by climate change, and coastal urban centers are increasingly at risk for floods.

Julia Moriarity, a ���񱦵� researcher, uses process-based and statistical machine-learning modeling to understand how flooding affects coastal areas.

“When urban areas flood, you can have sewage systems flood, water-treatment plants flood, nuclear power plants flood, because all these facilities have to be located near water,” Moriarty says. “So, the question is: when a flood causes polluted water to enter the local waterways, what’s that polluted water’s fate?”

Not only can floods contaminate local waterways by spreading bacterial or even radioactive contaminants into them, but they can unleash a cascade of events in which excess nutrient levels can stimulate harmful algae blooms, reduce oxygen levels in the water and reduce water clarity and quality, sometimes leading to “dead zones.”

Moriarty’s research combines process-based and statistical machine-learning modeling to analyze how floods of coastal infrastructure affect pollutant and nutrient fluxes in local waterways, and their impact on biogeochemical processes. A significant aim is to better understand how extreme floods degrade water quality and which aspects of flooding are predictable and which are not.

“If something’s predictable, it’s a lot easier to plan for it,” Moriarty says.

The research will use Baltimore, Maryland, as a case study, in collaboration with the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory. Using data from the climate model, as well as a new Baltimore hydrodynamic-biogeochemistry model, Moriarty and her research team aim to better understand how coastal urban flooding impacts local waterway biogeochemistry in different climate scenarios.

Further, the researchers want to use a combination of machine learning and sensitivity tests of the process-based model they develop to scale up what they learn from local observations in Baltimore to coastal-urban systems worldwide.

“The better we can understand and predict these events, the better we can plan for them,” Moriarty says. “It costs a lot less to mitigate risks in advance of events than to clean them up afterward.”

Top image: Glenn Asakawa/���񱦵�

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