Computational Modeling

Numerical simulations of three-dimensional ion crystal dynamics in a Penning trap using the fast multipole method

Daniel Morton — Tue, 08 Apr 2025 21:53:20 +0000

Numerical simulations of three-dimensional ion crystal dynamics in a Penning trap using the fast multipole method Daniel Morton Tue, 04/08/2025 - 15:53

JOURNAL OF PLASMA PHYSICS, 2025, 91, 2, E53

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Quantum simulation of nonlinear dynamical systems using repeated measurement

Daniel Morton — Mon, 31 Mar 2025 21:46:40 +0000

Quantum simulation of nonlinear dynamical systems using repeated measurement Daniel Morton Mon, 03/31/2025 - 15:46

JOURNAL OF PLASMA PHYSICS, 2025, 91, 2, E49

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Big Award for Understanding the Tiny

Daniel Morton — Tue, 25 Mar 2025 19:26:24 +0000

Big Award for Understanding the Tiny Daniel Morton Tue, 03/25/2025 - 13:26

Tara McMurty

RASEI Fellow Steven Spurgeon has been awarded the Burton Medal for his work harnessing AI and Machine Learning to accelerate the study of the microscopic world of energy materials

By Tara McMurty | Adapted from an article originally published by the National Renewable Energy Laboratory

Find out more

Original NREL Article

MSA Press Release

National Renewable Energy Laboratory (NREL) materials data scientist and RASEI Fellow Steven R. Spurgeon has been honored with the Microscopy Society of America’s (MSA’s) Burton Medal award. The award, which is given annually to a single physical sciences researcher under the age of 40, is the highest honor the MSA bestows upon early-career scientists in the field of microscopy and microanalysis.

“I’ve been involved with the MSA for 15 years and have been fortunate to work with amazing leaders in the field of microscopy,” Spurgeon said. “To be recognized with this award and join their ranks is a true honor.”

Throughout his career, Spurgeon has pioneered the integration of machine learning (ML) with electron microscopy, using artificial intelligence (AI), to help make sense of the detailed images generated by electron microscopes. This approach not only dramatically enhances the efficiency of microscopy techniques; it also provides new insights into the behavior of functional materials—like silicon microprocessors used in computers and cell phones—at the atomic level. These insights enable researchers to fine-tune material properties and enhance efficiency to drive advancements in energy solutions.

“Dr. Spurgeon’s pioneering work at the intersection of AI and microscopy continues to transform materials science,” said Katherine Jungjohann, who manages NREL’s Analytical Microscopy and Imaging Science group. “His visionary leadership and groundbreaking research make him a truly deserving recipient of the Burton Medal.”

Spurgeon’s journey to the AI/microscopy frontier began with a deep curiosity about the fundamental building blocks of matter. As a graduate student at Drexel University, he studied materials science and engineering, focusing on developing new functional materials. After Spurgeon completed his Ph.D., he joined Pacific Northwest National Laboratory (PNNL), first as a postdoctoral research associate, and later as a staff scientist in the laboratory’s longstanding thin-film Basic Energy Sciences program. At PNNL, he developed new functional materials that could be used for energy storage and computing, which was recognized by the PNNL Laboratory Director’s Award for Early Career Achievement. He also had a realization that would shape the trajectory of his career.

AI-powered tools like transformers—which is what the “T” in ChatGPT stands for—began to emerge in the scientific community about 10 years ago and were used in areas like processing internet data and building autonomous vehicles. Watching the rise of these ML tools, Spurgeon began to wonder if they could benefit his own field of work.

“I realized we were collecting and analyzing all our data by hand,” Spurgeon recalled. “That made me ask, ‘Can we use AI to accelerate our experiments so that humans don’t have to make every single decision?’ I saw that machine learning could help us analyze larger datasets, uncover patterns that would be difficult to detect manually, and ultimately shorten the time to discovery.”

Inspired by these possibilities, Spurgeon helped launch an AI initiative at PNNL and established a groundbreaking partnership with industry to design a completely new AI-infused microscope. His efforts led to the development of the Autonomous Electron Microscope (AutoEM), a platform that leverages AI to improve the way researchers study and understand functional materials. The platform, which enables researchers to conduct analyses up to 1,000 times faster than traditional methods, earned Spurgeon and his team an R&D 100 Award in 2024.

“During his time at PNNL, Dr. Spurgeon rapidly ascended to international prominence in AI-guided materials science and electron microscopy,” said Sergei Kalinin, chief scientist of Physics-Informed Machine Learning at PNNL. “He established himself as a brilliant researcher, a staunch advocate for our field, and an exceptional mentor, and he continues this reputation today.”

After nearly 10 years at PNNL, Spurgeon joined NREL’s Material Science team in May 2024 with a mission to lead research on autonomous materials science and characterization in the development of new energy technologies.

“I joined NREL to help establish a forward-thinking vision for autonomous science,” Spurgeon said. “NREL's leadership in emerging energy technologies, coupled with its proactive approach to integrating AI, creates an environment like no other, where researchers can strategically innovate and push the boundaries of energy solutions.”

Since joining NREL, Spurgeon has integrated autonomous capabilities into lab workflows, established new industry partnerships, and created effective teams. In May 2025, he is organizing a workshop to convene leading experts to explore practical, real-world applications of autonomous research.

His work has sped up experiments and led to faster discoveries in energy materials and microelectronics, which translates to tangible benefits for NREL’s partners and the wider community. Moving forward, he is focused on using AI to develop important materials—like advanced semiconductors and catalysts—that could lead to major breakthroughs in technology.

As Spurgeon explained, “AI-driven autonomy in materials science is the key to breaking through current research bottlenecks. It allows us to move beyond incremental improvements and achieve truly transformative discoveries in the energy materials we use every day, saving money and improving resilience.”

Reflecting on his career, Spurgeon identified the thrill of discovery, bolstered by persistence in the face of failure, as a driving factor in all that he has achieved.

“Breakthroughs don’t come easily. They often follow many, many failures,” Spurgeon said. “But every once in a while, you get a new process to work or you uncover a phenomenon no one has seen before. Those moments of seeing something for the first time—something no one else has seen—are what make me come to work every day.”

Beyond the personal satisfaction of pushing the boundaries of knowledge, Spurgeon finds inspiration in the support of the scientific community and in the impact of his work on real-world technologies.

“When you’re all pushing in the same direction, you can help each other, share in the struggles, and celebrate the wins,” Spurgeon said. “It has been especially rewarding to work with so many talented early-career staff and students over the years.”

Spurgeon also recognizes that the focus of his work—AI integration—has been the subject of a fair amount of debate in recent years.

“We're at the start of a significant transformation in science, but the essence of the scientific process—generating and testing a hypothesis—still belongs to humans,” Spurgeon said. “AI can help us analyze more data and refine our decision-making, but it’s still on us as scientists to take responsibility for our conclusions.”

Daniel Morton — Thu, 19 Dec 2024 15:03:00 +0000

Innovative Wind Turbine Control Earns Lucy Pao Prestigious IEEE Award Daniel Morton Thu, 12/19/2024 - 08:03

Daniel Morton

Renewable and Sustainable Energy Institute (RASEI) Fellow Lucy Pao, from the Department of Electrical, Computer, and Energy Engineering at University of Colorado Boulder, is travelling to Milan, Italy, this December to receive the IEEE Transactions on Control Systems Technology Outstanding Paper Award.

Find out more

IEEE Conference on Decision and Control

Read the Article

ForWind Center

Collaboration Highlight

Each year a panel reviews papers nominated from the past two years and selects an article of high significance. The paper, published in 2022, describes the first experimental implementation of Model Predictive Control of the pitch angles of the blades in wind turbines. The Award Ceremony will be held on December 18, 2024, as part of the 2024 IEEE Conference on Decision and Control, held at the Milan Convention Centre in Italy.

Model Predictive Control (MPC) is an established control technique that is popular in the general control systems community. One of the primary demonstrated applications for MPC is in process chemistry for the large-scale manufacture of pharmaceuticals and commodity chemicals. This paper describes, for the first time, the application of this technique in the blade pitch control of wind turbines. Control systems in wind turbines are essential for optimizing their performance, ensuring safety, and maximizing energy output. By monitoring key parameters such as blade pitch, rotor speed, yaw angle, and blade root bending moment, the control system can adapt to changing wind conditions, enhancing the efficiency of power generation and reducing mechanical stress on components, helping to extend the turbine’s lifespan.

“MPC is a very nice approach, but it is computationally complex,” explained Lucy. “There is an optimization that needs to be done at every time step, so getting it into a form where we could implement it, was a challenge”.

While MPC is a computationally intensive approach, the benefits for wind turbines are significant. Many wind turbine control systems are based on a feedback-only system, where the adjustments are based on feedback of what is happening now. MPC enables preview information to be included, so, for example, you can incorporate knowledge of a gust coming through and the controls can prepare the turbine ahead of time, which can reduce structural loading on that turbine. It changes the control system from simply being responsive to stimuli, to instead being proactive to knowledge of the conditions, essentially letting the wind turbine “see into the future”.

The MPC approach could have significant impacts on how wind turbines are controlled, not only improving their efficiency, but also reducing structural stress on the turbines and extending their lifetimes.

“We are really very excited about this award,” said Pao. “This has been a wonderful collaboration with our colleagues at the ForWind Center for Wind Energy Research in Oldenburg Germany. We started in 2016, with an experimental campaign in 2019 that led to this paper”. Misha Sinner, the lead author of the study, was a graduate student working with Lucy Pao and is now a Research Engineer at the National Renewable Energy Laboratory (NREL). Misha travelled to Germany to lead this experimental campaign, and worked together with the team in Germany, led by Martin Kühn, to apply MPC to the turbines. “This was a complex experiment to run, and it wouldn’t have been possible without the team of experts and infrastructure at ForWind,” said Misha. “The collaboration has been a really symbiotic relationship; I really value Martin’s input, support, and feedback on the work,” said Lucy.

The collaboration was sparked over a meeting in Stuttgart in 2015. “Martin and I were both external evaluators on a PhD defense for a student at the University of Stuttgart. We had lots of opportunities to chat, and when I mentioned that I was coming up on a sabbatical, he suggested a fellowship that I could apply for that would enable me to work with them,” explained Lucy. This was the start of the long and fruitful collaboration among the team.

There is hope that the recognition that the award brings will accelerate adoption of advanced control systems by the wind energy industry. “MPC can take into account constraints on the wind turbine system, and it is flexible enough to incorporate preview wind information,” said Lucy. This could provide significant improvements in the efficiency and lifetime of future wind turbines. “It is a really nice control framework, and while it has been looked at in the simulation context, it had not been examined experimentally for blade pitch control, to our knowledge,” said Lucy. “We have heard that Industry is looking at MPC, but we don’t know whether it has made it into their actual controllers. The wind turbine industry can be quite secretive about what they are doing”.

The MPC approach could have significant impact on future, and existing wind turbines, by giving the control system the chance to effectively “see into the future” by a couple to tens of seconds so the turbine can prepare itself. “We are very excited and really touched by this award,” emphasized Lucy, with the hope that this will catch the attention of those in the wind turbine industry.

December 2024

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Photocatalytic C–F bond activation in small molecules and polyfluoroalkyl substances

Daniel Morton — Wed, 20 Nov 2024 18:50:57 +0000

Photocatalytic C–F bond activation in small molecules and polyfluoroalkyl substances Daniel Morton Wed, 11/20/2024 - 11:50

NATURE, 2024

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