MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants. 

Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also honored for separate projects.

The first 29 winning projects will support mathematicians and researchers at universities and organizations working to develop artificial intelligence systems that help advance mathematical discovery and research across several key tasks.

Roe and Sutherland, along with Chris Birkbeck of the University of East Anglia, will use their grant to boost automated theorem proving by building connections between the L-Functions and Modular Forms Database (LMFDB) and the Lean4 mathematics library (mathlib).

“Automated theorem provers are quite technically involved, but their development is under-resourced,” says Sutherland. With AI technologies such as large language models (LLMs), the barrier to entry for these formal tools is dropping rapidly, making formal verification frameworks accessible to working mathematicians. 

Mathlib is a large, community-driven mathematical library for the Lean theorem prover, a formal system that verifies the correctness of every step in a proof. Mathlib currently contains on the order of 105 mathematical results (such as lemmas, propositions, and theorems). The LMFDB, a massive, collaborative online resource that serves as a kind of “encyclopedia” of modern number theory, contains more than 109 concrete statements. Sutherland and Roe are managing editors of the LMFDB.

Roe and Sutherland’s grant will be used for a project that aims to augment both systems, making the LMFDB’s results available within mathlib as assertions that have not yet been formally proved, and providing precise formal definitions of the numerical data stored within the LMFDB. This bridge will benefit both human mathematicians and AI agents, and provide a framework for connecting other mathematical databases to formal theorem-proving systems.

The main obstacles to automating mathematical discovery and proof are the limited amount of formalized math knowledge, the high cost of formalizing complex results, and the gap between what is computationally accessible and what is feasible to formalize.

To address these obstacles, the researchers will use the funding to build tools for accessing the LMFDB from mathlib, making a large database of unformalized mathematical knowledge accessible to a formal proof system. This approach enables proof assistants to identify specific targets for formalization without the need to formalize the entire LMFDB corpus in advance.

“Making a large database of unformalized number-theoretic facts available within mathlib will provide a powerful technique for mathematical discovery, because the set of facts an agent might wish to consider while searching for a theorem or proof is exponentially larger than the set of facts that eventually need to be formalized in actually proving the theorem,” says Roe.

The researchers note that proving new theorems at the frontier of mathematical knowledge often involves steps that rely on a nontrivial computation. For example, Andrew Wiles’ proof of Fermat’s Last Theorem uses what is known as the “3-5 trick” at a crucial point in the proof.

“This trick depends on the fact that the modular curve X_0(15) has only finitely many rational points, and none of those rational points correspond to a semi-stable elliptic curve,” according to Sutherland. “This fact was known well before Wiles’ work, and is easy to verify using computational tools available in modern computer algebra systems, but it is not something one can realistically prove using pencil and paper, nor is it necessarily easy to formalize.”

While formal theorem provers are being connected to computer algebra systems for more efficient verification, tapping into computational outputs in existing mathematical databases offers several other benefits.

Using stored results leverages the thousands of CPU-years of computation time already spent in creating the LMFDB, saving money that would be needed to redo these computations. Having precomputed information available also makes it feasible to search for examples or counterexamples without knowing ahead of time how broad the search can be. In addition, mathematical databases are curated repositories, not simply a random collection of facts. 

“The fact that number theorists emphasized the role of the conductor in databases of elliptic curves has already proved to be crucial to one notable mathematical discovery made using machine learning tools: murmurations,” says Sutherland.

“Our next steps are to build a team, engage with both the LMFDB and mathlib communities, start to formalize the definitions that underpin the elliptic curve, number field, and modular form sections of the LMFDB, and make it possible to run LMFDB searches from within mathlib,” says Roe. “If you are an MIT student interested in getting involved, feel free to reach out!” 

Police are using their drones as flying automated license plate readers (ALPRs), airborne police cameras that make it easier than ever for law enforcement to follow you. 

"The Flock Safety drone, specifically, are flying LPR cameras as well,” Rahul Sidhu, Vice President of Aviation at Flock Safety, recently told a group of potential law enforcement customers interested in drone-as-first-responder (DFR) programs. 

The integration of Flock Safety’s flagship ALPR technology with its Aerodome drone equipment is a police surveillance combo poised to elevate the privacy threats to civilians caused by both of these invasive technologies as drone adoption expands. 

flock_drone_flying_police_platform.png

A slide from a Flock Safety presentation to Rutherford County Sheriff's Office in North Carolina, obtained via public records, featuring Flock Safety products, including the Aerodome drone and the Wing product, which helps convert surveillance cameras into ALPR systems

The use of DFR programs has grown exponentially. The biggest police technology companies, like Axon, Flock Safety, and Motorola Solutions, are broadening their drone offerings, anticipating that drones could become an important piece of their revenue stream. 

Communities must demand restrictions on how local police use drones and ALPRs, let alone a dangerous hybrid of the two. Otherwise, we can soon expect that a drone will fly to any call for service and capture sensitive location information about every car in its flight path, capturing more ALPR data to add to the already too large databases of our movements. 

ALPR systems typically rely on cameras that have been fixed along roadways or attached to police vehicles. These cameras capture the image of a vehicle, then use artificial intelligence technology to log the license plate, make, model, color, and other unique identifying information, like dents and bumper stickers. This information is usually stored on the manufacturer’s servers and often made available on nationwide sharing networks to police departments from other states and federal agencies, including Immigration and Customs Enforcement. ALPRs are already used by most of the largest police departments in the country, and Flock Safety also now offers the ability for an agency to turn almost any internet-enabled cameras into an ALPR camera. 

ALPRs present a host of problems. ALPR systems vacuum up data—like the make, model, color, and location of vehicles—on people who will never be involved in a crime, used in gridding areas to systematically make a record of when and where vehicles have been. ALPRs routinely make mistakes, causing police to stop the wrong car and terrorize the driver. Officers have abused law enforcement databases in hundreds of cases. Police have used them to track across state lines people seeking legal health procedures. Even when there are laws against sharing data from these tools with other departments, some policing agencies still do.

Drones, meanwhile, give police a view of roofs, backyards, and other fenced areas where cops can’t casually patrol, and their adoption is becoming more common. Companies that sell drones have been helping law enforcement agencies to get certifications from the Federal Aviation Authority (FAA), and recently-implemented changes to the restrictions on flying drones beyond the visual line of sight will make it even easier for police to add this equipment. According to the FAA, since a new DFR waiver process was implemented in May 2025, the FAA has granted more than 410 such waivers, already accounting for almost a third of the approximately 1,400 DFR waivers that have been granted since such programs began in 2018.

Local officials should, of course, be informed that the drones they’re buying are equipped to do such granular surveillance from the sky, but it is not clear that this is happening. While the ALPR feature is available as part of Flock drone acquisitions, some government customers may not realize that to approve a drone from Flock Safety may also mean approving a flying ALPR. And though not every Flock safety drone is currently running the ALPR feature, some departments, like Redondo Beach Police Department, have plans to activate it in the near future. 

ALPRs aren’t the only so-called payloads that can be added to a drone. In addition to the high resolution and thermal cameras with which drones can already be equipped, drone manufacturers and police departments have discussed adding cell-site simulators, weapons, microphones, and other equipment. Communities must mobilize now to keep this runaway surveillance technology under tight control.

When EFF posed questions to Flock Safety about the integration of ALPR and its drones, the company declined to comment.

Mapping, storing, and tracking as much personal information as possible—all without warrants—is where automated police surveillance is heading right now. Flock has previously described its desire to connect ALPR scans to additional information on the person who owns the car, meaning that we don’t live far from a time when police may see your vehicle drive by and quickly learn that it’s your car and a host of other details about you. 

EFF has compiled a list of known drone-using police departments. Find out about your town’s surveillance tools at the Atlas of Surveillance. Know something we don't? Reach out at aos@eff.org.

Details from leaked documents:

While people often look at China’s Great Firewall as a single, all-powerful government system unique to China, the actual process of developing and maintaining it works the same way as surveillance technology in the West. Geedge collaborates with academic institutions on research and development, adapts its business strategy to fit different clients’ needs, and even repurposes leftover infrastructure from its competitors.

[…]

The parallels with the West are hard to miss. A number of American surveillance and propaganda firms also started as academic projects before they were spun out into startups and grew by chasing government contracts. The difference is that in China, these companies operate with far less transparency. Their work comes to light only when a trove of documents slips onto the internet...

The deadline has passed for his Cabinet to identify treaties like the U.N. Framework Convention on Climate Change they want axed.

The Charybdis was expected to facilitate a wind power building boom. Now, its future is clouded as the president wages war on the industry.

A new report warns that plans for oil, gas and coal will make it harder to limit global warming to 2 degrees Celsius or less.

A program approved by the Legislature would mirror efforts in Alabama and Louisiana that help homeowners install wind-resistant roofs.

Lower gasoline prices and higher electricity costs have negated EVs' financial advantage. Those factors can change quickly.

The country’s third party said the policy, adopted in 2019, is “no longer practically possible."

“Climate change is rapidly and actively changing the geopolitical dynamics” in the region, an Arctic researcher says.

Scientists at China’s Tsinghua University found that early deaths could rise to six times the levels of 2010-2014 by next century.

The Chinese manufacturer participated in project bidding for the 26-megawatt turbine, obtaining “a small batch” of orders, said an official.

The Southeast Asian city-state aims to boost trade with Africa and meet its own net-zero ambitions.

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.

But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle. That’s too bad, because humans could use the help. For example, after a decade of research into a class of materials that could revolutionize quantum computing, called quantum spin liquids, only a dozen material candidates have been identified. The bottleneck means there are fewer materials to serve as the basis for technological breakthroughs.

Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules. The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.

“The models from these large companies generate materials optimized for stability,” says Mingda Li, MIT’s Class of 1947 Career Development Professor. “Our perspective is that’s not usually how materials science advances. We don’t need 10 million new materials to change the world. We just need one really good material.”

The approach is described today in a paper published by Nature Materials. The researchers applied their technique to generate millions of candidate materials consisting of geometric lattice structures associated with quantum properties. From that pool, they synthesized two actual materials with exotic magnetic traits.

“People in the quantum community really care about these geometric constraints, like the Kagome lattices that are two overlapping, upside-down triangles. We created materials with Kagome lattices because those materials can mimic the behavior of rare earth elements, so they are of high technical importance.” Li says.

Li is the senior author of the paper. His MIT co-authors include PhD students Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, and Denisse Cordova Carrizales; postdoc Manasi Mandal; undergraduate researchers Kiran Mak and Bowen Yu; visiting scholar Nguyen Tuan Hung; Xiang Fu ’22, PhD ’24; and professor of electrical engineering and computer science Tommi Jaakkola, who is an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society. Additional co-authors include Yao Wang of Emory University, Weiwei Xie of Michigan State University, YQ Cheng of Oak Ridge National Laboratory, and Robert Cava of Princeton University.

Steering models toward impact

A material’s properties are determined by its structure, and quantum materials are no different. Certain atomic structures are more likely to give rise to exotic quantum properties than others. For instance, square lattices can serve as a platform for high-temperature superconductors, while other shapes known as Kagome and Lieb lattices can support the creation of materials that could be useful for quantum computing.

To help a popular class of generative models known as a diffusion models produce materials that conform to particular geometric patterns, the researchers created SCIGEN (short for Structural Constraint Integration in GENerative model). SCIGEN is a computer code that ensures diffusion models adhere to user-defined constraints at each iterative generation step. With SCIGEN, users can give any generative AI diffusion model geometric structural rules to follow as it generates materials.

AI diffusion models work by sampling from their training dataset to generate structures that reflect the distribution of structures found in the dataset. SCIGEN blocks generations that don’t align with the structural rules.

To test SCIGEN, the researchers applied it to a popular AI materials generation model known as DiffCSP. They had the SCIGEN-equipped model generate materials with unique geometric patterns known as Archimedean lattices, which are collections of 2D lattice tilings of different polygons. Archimedean lattices can lead to a range of quantum phenomena and have been the focus of much research.

“Archimedean lattices give rise to quantum spin liquids and so-called flat bands, which can mimic the properties of rare earths without rare earth elements, so they are extremely important,” says Cheng, a co-corresponding author of the work. “Other Archimedean lattice materials have large pores that could be used for carbon capture and other applications, so it’s a collection of special materials. In some cases, there are no known materials with that lattice, so I think it will be really interesting to find the first material that fits in that lattice.”

The model generated over 10 million material candidates with Archimedean lattices. One million of those materials survived a screening for stability. Using the supercomputers in Oak Ridge National Laboratory, the researchers then took a smaller sample of 26,000 materials and ran detailed simulations to understand how the materials’ underlying atoms behaved. The researchers found magnetism in 41 percent of those structures.

From that subset, the researchers synthesized two previously undiscovered compounds, TiPdBi and TiPbSb, at Xie and Cava’s labs. Subsequent experiments showed the AI model’s predictions largely aligned with the actual material’s properties.

“We wanted to discover new materials that could have a huge potential impact by incorporating these structures that have been known to give rise to quantum properties,” says Okabe, the paper’s first author. “We already know that these materials with specific geometric patterns are interesting, so it’s natural to start with them.”

Accelerating material breakthroughs

Quantum spin liquids could unlock quantum computing by enabling stable, error-resistant qubits that serve as the basis of quantum operations. But no quantum spin liquid materials have been confirmed. Xie and Cava believe SCIGEN could accelerate the search for these materials.

“There’s a big search for quantum computer materials and topological superconductors, and these are all related to the geometric patterns of materials,” Xie says. “But experimental progress has been very, very slow,” Cava adds. “Many of these quantum spin liquid materials are subject to constraints: They have to be in a triangular lattice or a Kagome lattice. If the materials satisfy those constraints, the quantum researchers get excited; it’s a necessary but not sufficient condition. So, by generating many, many materials like that, it immediately gives experimentalists hundreds or thousands more candidates to play with to accelerate quantum computer materials research.”

“This work presents a new tool, leveraging machine learning, that can predict which materials will have specific elements in a desired geometric pattern,” says Drexel University Professor Steve May, who was not involved in the research. “This should speed up the development of previously unexplored materials for applications in next-generation electronic, magnetic, or optical technologies.”

The researchers stress that experimentation is still critical to assess whether AI-generated materials can be synthesized and how their actual properties compare with model predictions. Future work on SCIGEN could incorporate additional design rules into generative models, including chemical and functional constraints.

“People who want to change the world care about material properties more than the stability and structure of materials,” Okabe says. “With our approach, the ratio of stable materials goes down, but it opens the door to generate a whole bunch of promising materials.”

The work was supported, in part, by the U.S. Department of Energy, the National Energy Research Scientific Computing Center, the National Science Foundation, and Oak Ridge National Laboratory.

Nature Climate Change, Published online: 22 September 2025; doi:10.1038/s41558-025-02409-8

Natural disturbances, such as windthrows, pest outbreaks and wildfires, pose a major economic threat for the forestry sector. By coupling spatially explicit ecological and economic forest models, this study assesses the costs of natural disturbances under current and future climate conditions for all of Europe.

Nature Climate Change, Published online: 22 September 2025; doi:10.1038/s41558-025-02447-2

C40 is a global network of mayors united in a commitment to climate change action. Since its inception in 2005, C40 has grown to include nearly 100 of the world’s cities, maintaining high standards that focus on inclusivity, collaboration and science-based approaches to combat climate change. We interviewed members of the C40 organization, including mayors of its member cities, to ask about the history, success and challenges of C40, and their plans for future action.

Nature Climate Change, Published online: 22 September 2025; doi:10.1038/s41558-025-02423-w

Private sectors play an important role in global adaptation efforts, yet we have a limited understanding of their investment patterns. With firm adaptation expenditure data across five coastal urban areas, this research shows how adaptation investment differs across regions and sectors.

Nature Climate Change, Published online: 22 September 2025; doi:10.1038/s41558-025-02435-6

Coastal settlement retreat reflects human behavioural adaptation to increasing coastal climate hazards. Using night-time light data over 1992–2019, this study finds that over half of global coastal settlements have retreated, driven by insufficient infrastructure protection and adaptive capacity.

The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship. Over the last few years, no technology has changed that craft more than artificial intelligence.

While many are predicting a rapid and complete transformation in how startups are built, the Trust Center’s leaders have a more nuanced view.

“The fundamentals of entrepreneurship haven’t changed with AI,” says Trust Center Entrepreneur in Residence Macauley Kenney. “There’s been a shift in how entrepreneurs accomplish tasks, and that trickles down into how you build a company, but we’re thinking of AI as another new tool in the toolkit. In some ways the world is moving a lot faster, but we also need to make sure the fundamental principles of entrepreneurship are well-understood.”

That approach was on display during this summer’s delta v startup accelerator program, where many students regularly turned to AI tools but still ultimately relied on talking to their customers to make the right decisions for their business.

Students in this year’s cohort used AI tools to accelerate their coding, draft presentations, learn about new industries, and brainstorm ideas. The Trust Center is encouraging students to use AI as they see fit while also staying mindful of the technology’s limitations.

The Trust Center itself has also embraced AI, most notably through Jetpack, its generative AI app that walks users through the 24 steps of disciplined entrepreneurship outlined in Managing Director Bill Aulet’s book of the same name. When students input a startup idea, the tool can suggest customer segments, early markets to pursue, business models, pricing, and a product plan.

The ways the Trust Center wants students to use Jetpack is apparent in its name: It’s inspired by the acceleration a jetpack provides, but users still need to guide its direction.

Even with AI technology’s current limitations, the Trust Center’s leaders acknowledge it can be a powerful tool for people at any stage of building a business, and their use of AI will continue to evolve with the technology.

“It’s undeniable we’re in the midst of an AI revolution right now,” says Entrepreneur in Residence Ben Soltoff. “AI is reshaping a lot of things we do, and it’s also shaping how we do entrepreneurship and how students build companies. The Trust Center has recognized that for years, and we’ve welcomed AI into how we teach entrepreneurship at all levels, from the earliest stages of idea formation to exploring and testing those ideas and understanding how to commercialize and scale them.”

AI’s strengths and weaknesses

For the past few years, when the Trust Center’s delta v staff get together for strategic retreats, AI has been a central topic. The delta v program’s organizers think about how students can get the most out of the technology each year as they plan their summer-long curriculum.

Everything starts with Orbit, the mobile app designed to help students find entrepreneurial resources, network with peers, access mentorship, and identify events and jobs. Jetpack was added to Orbit last year. It is trained on Aulet’s “Disciplined Entrepreneurship” as well as former Trust Center Executive Director Paul Cheek’s “Startup Tactics” book.

The Trust Center describes Jetpack’s outputs as first drafts designed to help students brainstorm their next steps.

“You need to verify everything when you are using AI to build a business,” says Kenney, who is also a lecturer at MIT Sloan and MIT D-Lab. “I have yet to meet anyone who will base their business on the output of something like ChatGPT without verifying everything first. Sometimes, the verification can take longer than if you had done the research yourself from the beginning.”

One company in this year’s cohort, Mendhai Health, uses AI and telehealth to offer personalized physical therapy for women struggling with pelvic floor dysfunction before and after childbirth.

“AI has definitely made the entrepreneurial process more efficient and faster,” says MBA student Aanchal Arora. “Still, overreliance on AI, at least at this point, can hamper your understanding of customers. You need to be careful with every decision you make.”

Kenney notes the way large language models are built can make them less useful for entrepreneurs.

“Some AI tools can increase your speed by doing things like automatically sorting your email or helping you vibe code apps, but many AI tools are built off averages, and those can be less effective when you’re trying to connect with a very specific demographic,” Kenney says. “It’s not helpful to have AI tell you about an average person, you need to personally have strong validation that your specific customer exists. If you try to build a tool for an average person, you may build a tool for no one at all.”

Students eager to embrace AI may also be overwhelmed by the sheer volume of tools available today. Fortunately, MIT students have a long history of being at the forefront of any new technology, and this year’s delta v cohort featured teams leveraging AI at the core of their solutions and in every step of their entrepreneurial journeys.

MIT Sloan MBA candidate Murtaza Jameel, whose company Cognify uses AI to simulates user interactions with websites and apps to improve digital experiences, describes his firm as an AI-native business.

“We’re building a design intelligence tool that replaces product testing with instant, predictive simulations of user behavior,” Jameel explains. “We’re trying to integrate AI into all of our processes: ideation, go to market, programming. All of our building has been done with AI coding tools. I have a custom bot that I’ve fed tons of information about our company to, and it’s a thought partner I’m speaking to every single day.”

The more things change…

One of the fundamentals the Trust Center doesn’t see changing is the need for students to get out of the lab or the classroom to talk to customers.

“There are ways that AI can unlock new capabilities and make things move faster, but we haven’t turned our curriculum on its head because of AI,” Soltoff says. “In delta v, we stress first and foremost: What are you building and who are you building it for? AI alone can’t tell you who your customer is, what they want, and how you can better serve their needs. You need to go out into the world to make that happen.”

Indeed, many of the biggest hurdles delta v teams faced this summer looked a lot like the hurdles entrepreneurs have always faced.

“We were prepared at the Trust Center to see a big change and to adapt to that, but the companies are still building and encountering the same challenges of customer identification, beachhead market identification, team dynamics,” Kenney says. “Those are still the big meaty challenges they’ve always been working on.”

Amid endless hype about AI agents and the future of work, many founders this summer still said the human side of delta v is what makes the program special.

“I came to MIT with one goal: to start a technology company,” Jameel says. “The delta v program was on my radar when I was applying to MIT. The program gives you incredible access to resources — networks, mentorship, advisors. Some of the top folks in our industry are advising us now on how to build our company. It’s really unique. These are folks who have done what you’re doing 10 or 20 years ago, all just rooting for you. That’s why I came to MIT.”

In recent years, power outages caused by extreme weather or substation attacks have exposed the vulnerability of the electric grid. For the nation’s military bases, which are served by the grid, being ready for outages is a matter of national security. What better way to test readiness than to cut the power?

Lincoln Laboratory is doing just that with its Energy Resilience Readiness Exercises (ERREs). During an exercise, a base is disconnected from the grid, testing the ability of backup power systems and service members to work through failure. Lasting up to 15 hours, each exercise mimics a real outage event with limited forewarning to the base population.

“No one thought that this kind of real-world test would be accepted. We’ve now done it at 33 installations, impacting over 800,000 people,” says Jean Sack ’13, SM ’15, who leads the program with Christopher Lashway and Annie Weathers in the laboratory's Energy Systems Group.

According to a Department of Energy report, 70 percent of the nation’s transmission lines are approaching end of life. This aging infrastructure, combined with increasing power demands and interdependencies, threatens cascading failures. In response, the Department of Defense (DoD) has sharpened its focus on energy resilience, or the ability to anticipate, withstand, and recover from outages. On a base, an outage could disrupt critical missions, open the door to physical or cyberattacks, and cut off water supplies.

“Threats to this already-fragile system are increasing. That's why this work is so important,” Sack says. 

Safely cutting power

Before an exercise, the laboratory team works closely with base leadership and infrastructure personnel to carefully plan how it will safely disconnect from utility power. Over multiple site visits, they study each building and mission to understand power capabilities, ensure health and safety, and develop contingency plans.

“We get people together who may never have spoken before, but depend on one another. We like to say ‘connecting mission owners to their utility providers,’” says Lashway, a former electrician turned energy-systems researcher. “The planning process is a huge learning opportunity, and a chance to fix issues ahead of the outage.”

On the day of the outage, laboratory staff are on site to ensure the process runs smoothly, but the base is meant to run the exercise. Since beginning in 2018, the ERRE campaign has reached huge installations, including Fort Bragg, a U.S. Army base in North Carolina that sees nearly 150,000 people daily, and sites as far away as England and Japan.

The key is to not limit its scope. All facilities and missions, especially those that are critical, should be included, and service members are tasked with working through issues. To make exercises even more useful as an evaluation of readiness, some are modified with scripted scenarios simulating real-world incidents. These scenarios might challenge personnel to handle a cyberattack to control systems, shutdown of a backup power plant, or a rocket launch during an outage.

“We can do all the tabletop exercises in the world, but when you actually pull the plug, the question is, what actually goes on?” former assistant secretary of defense for sustainment Robert McMahon said at a joint House Armed Services subcommittee hearing about initial exercises. “Perhaps the most important lesson that I've seen is a lack of appreciation and understanding by our senior leaders at the installation level, all the way up to my level, of what we thought was going to happen versus what actually occurred, and then being able to apply those lessons learned.”

Illuminating issues

The ERREs have brought to light common issues across bases. One of them is a reliance on fragile or faulty backup systems. For example, electronic equipment experiences a hard shutdown if it isn't supported by a backup battery to bridge power transitions. In some instances, these battery systems failed or unexpectedly depleted due to age or generator issues. “We see a giant comms room drop out, and then phones and computers don’t work. It emphasizes the need for redundancies,” Lashway says.

Generators also present issues. Some fail because they aren’t regularly serviced or refueled through the long outage. Sometimes, personnel mistakenly assumed a generator would support their entire building, requiring reconfigurations after the fact. Air conditioning systems are often excluded from generator-supported emergency circuits, but rooms with a large number of computers generate a lot of heat, and overheated equipment quickly shuts down.

The exercises also unveiled interdependencies and chain reactions. In one case, a fire-suppression system accidentally went off, dousing a hangar in foam. The cause was a pressure drop at the same exact moment a switch reset.

“Executing an operation at this scale stresses how each of these factors need to work harmoniously and efficiently to ensure that the base, and ultimately missions, remain functional,” Lashway says.

Beyond resolving technical issues, the exercises have been valuable for practicing coordination and following chains of command. They’ve also revealed social challenges of operating through outages. For instance, some DoD guidance restricts the use of generators at daycare centers, so parents needed to coordinate care while maintaining their mission. 

After an exercise, the laboratory compiles all findings in a report for the base. It provides time stamps of significant events by building, identifies links between issues, and summarizes common problems site-wide. It then provides recommendations to address vulnerabilities. “Our goal is to provide as much justification as possible for the base to get the resources they need to fix a problem,” Sack says. 

The researchers also want to help bases prevent issues and avoid costly repairs. Recently, they’ve been using power meters to capture electrical data before, during, and after an exercise. These monitoring tools reveal power-quality issues that are otherwise hidden.

“Not all power is created equal, and standards must be followed to ensure equipment, especially specialized military equipment, operates properly and doesn’t get damaged over the long term. Power metering provides a view into that,” says Lashway.

Sparking resiliency ahead

Lincoln Laboratory’s ERRE campaign has resulted in legislation. In 2021, Congress passed a law requiring each military branch to perform at least five ERREs, or "Black Start Exercises," per year through 2027. That law was recently reauthorized until 2032. The team has transitioned the ERRE process to two private companies, as well as within the Air Force and Army, to conduct exercises in the coming years.

“It's very exciting that this got Congress' attention and has scaled across the DoD,” says Nick Judson, who leads the portfolio of energy, water, and natural hazard resilience efforts within the Energy Systems Group. “This idea started out as a way to enable change on DoD installations, and included a lot of difficult conversations about turning the power off to critical missions, and now we're seeing significant improvements to the readiness of bases and their missions.”

It may even be encouraging some healthy competition across the services, Lashway says. At a recent regional event in Colorado, three U.S. Space Force installations each vied to push the scope and duration of their exercises.

The team’s focus is now turning to related analysis, such as water resiliency. Water and wastewater systems are vulnerable to disruptions beyond power outages, including equipment failure, sabotage, or water source depletion.

“We are conducting tabletop exercises and workshops uniting stakeholders around the importance of water and wastewater systems to enable missions,” says Amelia Servi, who leads this work. “So far, we’ve seen great engagement from groups managing water systems who have been seeking funds to fix these aging systems, and from missions who have previously taken water for granted.”

They are also working on long-term energy planning, including ways for installations to be less dependent on the grid. One way is to install microgrids, which are self-sufficient systems that can tap into stored energy. According to Sack, microgrids are highly customized and complicated to operate, so one goal is to design a standardized system. The team's recent power-metering data is providing useful initial inputs into such a design.

The researchers are also considering how this work could improve energy resiliency for civilians. Large-scale exercises might not be feasible for the public, but they could be conducted in areas important to public safety, or in places that rely on military resources. During one exercise in Georgia, city residents partially depended upon a base's power plant, so that exercise included working with the city to ensure its resiliency to the outage.

“Striking that balance of testing readiness without causing harm is a big challenge in this field and a huge motivation for us,” Sack says. “We are encouraged by the outcomes. Our work is impacting the services at the highest level, rewriting infrastructure policy, and making sure people can better sustain operations during grid disruptions.”