Correlated Electrons “Tango” in a Perovskite Oxide at the Extreme Quantum Limit
Originally published by Oak Ridge National Laboratory.
A team led by the Department of Energy’s Oak Ridge National Laboratory has found a rare quantum material in which electrons move in coordinated ways, essentially “dancing.” Straining the material creates an electronic band structure that sets the stage for exotic, more tightly correlated behavior — akin to tangoing — among Dirac electrons, which are especially mobile electric charge carriers that may someday enable faster transistors. The results are published in the journal Science Advances.
“We combined correlation and topology in one system,” said co-principal investigator Jong Mok Ok, who conceived the study with principal investigator Ho Nyung Lee of ORNL. Topology probes properties that are preserved even when a geometric object undergoes deformation, such as when it is stretched or squeezed. “The research could prove indispensable for future information and computing technologies,” added Ok, a former ORNL postdoctoral fellow.
In conventional materials, electrons move predictably (for example, lethargically in insulators or energetically in metals). In quantum materials in which electrons strongly interact with each other, physical forces cause the electrons to behave in unexpected but correlated ways; one electron’s movement forces nearby electrons to respond.
To study this tight tango in topological quantum materials, Ok led the synthesis of an extremely stable crystalline thin film of a transition metal oxide. He and colleagues made the film using pulsed-laser epitaxy and strained it to compress the layers and stabilize a phase that does not exist in the bulk crystal. The scientists were the first to stabilize this phase.
Using theory-based simulations, co-principal investigator Narayan Mohanta, a former ORNL postdoctoral fellow, predicted the band structure of the strained material. “In the strained environment, the compound that we investigated, strontium niobate, a perovskite oxide, changes its structure, creating a special symmetry with a new electron band structure,” Mohanta said.
Different states of a quantum mechanical system are called “degenerate” if they have the same energy value upon measurement. Electrons are equally likely to fill each degenerate state. In this case, the special symmetry results in four states occurring in a single energy level.
“Because of the special symmetry, the degeneracy is protected,” Mohanta said. “The Dirac electron dispersion that we found here is new in a material.” He performed calculations with Satoshi Okamoto, who developed a model for discovering how crystal symmetry influences band structure.
“Think of a quantum material under a magnetic field as a 10-story building with residents on each floor,” Ok posited. “Each floor is a defined, quantized energy level. Increasing the field strength is akin to pulling a fire alarm that drives all the residents down to the ground floor to meet at a safe place. In reality, it drives all the Dirac electrons to a ground energy level called the extreme quantum limit.”
Lee added, “Confined here, the electrons crowd together. Their interactions increase dramatically, and their behavior becomes interconnected and complicated.” This correlated electron behavior, a departure from a single-particle picture, sets the stage for unexpected behavior, such as electron entanglement. In entanglement, a state Einstein called “spooky action at a distance,” multiple objects behave as one. It is key to realizing quantum computing.
“Our goal is to understand what will happen when electrons enter the extreme quantum limit, where we find phenomena we still don’t understand,” Lee said. “This is a mysterious area.”
Speedy Dirac electrons hold promise in materials including graphene, topological insulators and certain unconventional superconductors. ORNL’s unique material is a Dirac semimetal, in which electron valence and conduction bands cross and this topology yields surprising behavior. Ok led measurements of the Dirac semimetal’s strong electron correlations.
“We found the highest electron mobility in oxide-based systems,” Ok said. “This is the first oxide-based Dirac material reaching the extreme quantum limit.”
That bodes well for advanced electronics. Theory predicts that it should take about 100,000 tesla (a unit of magnetic measurement) for electrons in conventional semiconductors to reach the extreme quantum limit. The researchers took their strain-engineered topological quantum material to Eun Sang Choi of the National High Magnetic Field Laboratory at the University of Florida to see what it would take to drive electrons to the extreme quantum limit. There, he measured quantum oscillations showing the material would require only 3 tesla to achieve that.
Other specialized facilities allowed the scientists to experimentally confirm the behavior Mohanta predicted. The experiments occurred at low temperatures so that electrons could move around without getting bumped by atomic-lattice vibrations. Jeremy Levy’s group at the University of Pittsburgh and the Pittsburgh Quantum Institute confirmed quantum transport properties. With synchrotron x-ray diffraction, Hua Zhou at the Advanced Photon Source, a DOE Office of Science user facility at Argonne National Laboratory, confirmed that the material’s crystallographic structure stabilized in the thin film phase yielded the unique Dirac band structure. Sangmoon Yoon and Andrew Lupini, both of ORNL, conducted scanning transmission electron microscopy experiments at ORNL that showed that the epitaxially grown thin films had sharp interfaces between layers and that the transport behaviors were intrinsic to strained strontium niobate.
“Until now, we could not fully explore the physics of the extreme quantum limit due to the difficulties in pushing all electrons to one energy level to see what would happen,” Lee said. “Now, we can push all the electrons to this extreme quantum limit by applying only a few tesla of magnetic field in a lab, accelerating our understanding of quantum entanglement.”
The title of the Science Advances paper is “Correlated Oxide Dirac Semimetal in the Extreme Quantum Limit.”
The DOE Office of Science supported the research. High magnetic field measurements were performed at the National High Magnetic Field Laboratory, which is supported by the National Science Foundation and the state of Florida. The research used resources of the Advanced Photon Source, a DOE Office of Science user facility at Argonne National Laboratory; its extraordinary facility operations to provide beam time during the pandemic were supported in part by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on the response to COVID-19, with funding provided by the Coronavirus CARES Act.
UT-Battelle manages ORNL for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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Chinese media outlet Dongchedi closed down a real highway for a multi-day test of 36 different car driver assist systems in complicated, dangerous real-world driving situations, and most came up severely lacking – though Tesla escaped the tests relatively unscathed.
Over the years, we’ve seen our fair share of crash tests, often conducted in labs to detect the severity of a crash and the probability of injury to occupants. These tests focus on how well a car’s physical crash structures protect occupants, and occasionally other road-users, in the event of a crash.
Then there are “real-world” tests, like the famous “moose test” where a driver has to do a rapid direction change to avoid an intruding object in the road, testing vehicle dynamics and whether a car is able to handle quick changes in direction at high speed without rolling over.
More recently, crash tests have started to incorporate systems like Automatic Emergency Braking (AEB), which is intended to apply the brakes when a crash is imminent to reduce severity of the crash, or even more recently, advanced driver aids collectively known as Advanced Driver Assist Systems or ADAS.
You’ve likely heard of these ADAS systems before, usually offered and branded by individual automakers, like Tesla’s Full Self-Driving, BYD’s God’s Eye, or Mercedes’ Drive Pilot. They’re not actually self-driving (well, Drive Pilot can drive you in certain circumstances, and Tesla says unsupervised FSD is Coming This Time Next Year™… for about the tenth year running), but they can fully control the vehicle on highways.
All of these fit under a common umbrella of SAE Level 2 systems that are meant to take some of the responsibilities of driving away from a human driver and let a computer handle them. This can help reduce driving fatigue, but more importantly, could also lead to safer driving as computers don’t lose attention or get tired and can theoretically make decisions much faster than a human could – or at least, that’s what auto industry marketing would like you to believe.
Despite the theoretical superiority of these computer systems, in the real world, anyone who has had experience with them knows that they can be strangely indecisive, and will often make different decisions even when encountering the same situation multiple times. That applies to these level 2 systems, and also to true self-driving systems like Waymo’s level 4 system.
Even if you haven’t driven in one, you’re probably skeptical. By now, we’ve all seen the Tesla Robotaxi fail videos, and heard about Autopilot deaths (including ones that get wrongly credited to Autopilot despite just being pedal confusion).
We’ve also seen that Tesla Wile E. Coyote video, where American Youtuber Mark Rober tested Tesla’s vision-only approach versus the vision+LiDAR approach – the latter of which most professionals agree is a more robust solution.
But there have been some real tests missing, among all this: a real-world, on-highway comparative test of several brands of car, in various complicated but plausible situations, with other cars driving around nearby, with full ADAS system activation, by an independent source.
Well, in comes Dongchedi with a test that beats the scale of any we’ve seen yet, which it posted on its youtube channel, DCARSTUDIO.
It’s in the form of a 92-minute video, only available in Chinese language (with English subtitles), where DCAR ran 36 separate cars available in China through six different situations to see how ADAS performed. It’s a great video that merits a watch, even though the language barrier and length may be a tough sell.
And, spoiler alert: things didn’t go all that well for most of the cars tested.
The six tests went as follows, and most included other active vehicles nearby to increase complexity and realism:
- A situation where you are following a lead vehicle, and the lead vehicle suddenly darts out of lane into another lane, revealing a stopped car in front of you, with traffic on your left restricting your ability to swerve/merge.
- A temporary construction zone in your lane, with short lead-up, requiring a merge.
- A construction zone forcing a merge, but a stationary truck parked on the shoulder, partially intruding into the active lane, at night.
- A stationary car with lights off, parked across two lanes, simulating a recently-crashed vehicle, at night.
- A vehicle joining the highway from an on-ramp and aggressively merging across lanes into the left lane in front of you, with no ability to avoid to the left due to guardrails.
- A boar darting across the highway.
Each test is a clearly difficult situation, and one which has led to many accidents in real life with human drivers. And each is plausible, and I would even hazard to say that most of us have seen a situation similar to one of these with our own eyes while driving (even beyond the simple construction zone test).
But if ADAS is supposed to be better and faster than humans, it should be able to handle these challenges, right? That is, after all, how many people use these systems, and how automakers market them (which is currently subject to legal action in California).
What makes this test different than others that we’ve seen (for example, the Mark Rober video) is that it happened on an actual public highway. Some automakers restrict certain ADAS features to public roads, or specifically to public highways, which are well-marked and thus less likely to offer unpredictable situations to systems that are still not ready to brave chaotic city roads. Doing the test on an actual highway means that these systems can run at their full potential.
In each of the tests, a majority of the cars either failed miserably or only did so-so. It almost seemed at first like the tests were configured deliberately to be impossible by the ADAS systems – but in each test, a few cars ended up being able to avoid any accident, a few managed to reduce the accident to a minor and survivable collision, and sometimes a few even seemed to behave like a human would, stopping and then creeping around the obstacle in question in as safe a manner as they could.
Not all vehicles did all six tests, some due to damage that made it impossible for them to continue (e.g. the Mercedes C-Class broke its radar sensor on the boar test), and some because DCAR trimmed the field down to the best-performing vehicle of each brand for some of the more difficult tests, like the aggressive merging test. So, 216 crashes might be a little poetic license on DCAR’s part, but maybe they just didn’t want to spoil the results in the title.
Some vehicles also showed weirdly conflicting behavior between performance of the AEB and ADAS systems. Cars which DCAR had previously tested and given a passing grade due to their AEB performance seemed to do worse on ADAS than otherwise. For example, there was a moment when the Xiaomi SU7 indicated it was activating AEB during test 1, but then stopped decelerating for a few moments, then started to decelerate again but was unable to avoid a crash.
And another interesting pattern that revealed itself was that many of the systems tried to swerve first, and only after that would hit the brakes, to try to avoid crashing into an object ahead of them in the lane. Swerving is often a less-safe behavior, at least in the situations tested on crowded highways, because swerving can spread an incident to other lanes, and because you don’t always know what’s right beside you at all times, given you only have two eyes on a swivel.
ADAS systems theoretically don’t have this disadvantage, since they can have cameras and sensors all around the car.
And yet, despite having those sensors and knowing there was no space to move to the side into neighboring vehicles, quite often the cars would try to swerve into a side lane, making those cars have to take evasive action even if they were close to the median, and only after creating a more dangerous situation would they return back to their lane, attempt to brake, and be unable to do so because of the time spent swerving and unsettling the car’s mass, time which could have been better spent slowing the vehicle to avoid or minimize the severity of a collision. These systems which are supposed to think much faster than a human showed the same potentially fatal indecision that so many human drivers show.
Of course, the best way to avoid all of this is just to leave more space between yourself and the car ahead. DCAR’s test driver often mentioned that the vehicles seemed to be following far too close before these accidents happened.
In the end, across all of the tests, Tesla came out on top, with both the Model 3 and Model X passing 5/6 tests. But they failed different tests – with the Model X driving into a well-marked construction zone and the Model 3 recognizing but not slowing fast enough to avoid the boar (only one vehicle avoided the boar: the Model X).
This is an interesting result, because Tesla has a vision-only system, using cameras and no other sensors. The cars were equipped with a variety of systems, some vision-only and some also including LiDAR and radar. The LiDAR systems should have had the advantage during nighttime, though none of the tests happened in inclement weather (heavy rain and fog), which is where LiDAR really shines.
But Tesla also has more experience offering driver-assist systems than the other brands. Tesla has been offering some form of driver assist since 2014, which is well before many of these companies even existed. That, along with the millions of miles of data collected from its vehicle fleet, surely helped Tesla get its crown in these tests.
But despite Tesla’s high performance, there is still a worrying pattern among the tests – even Tesla’s. Because, strangely, even cars within the same brand showed wildly differing results on the same tests.
For example, the top-range Aito M9 passed 3/6 tests, but the next step down, the M8, passed 1/6 tests. The lower-end Aito, the M7, passed 2/5 tests, faring better than the M8. The Aito M9 has the most sophisticated system the brand offers, but still failed the construction truck test, while the M7 passed it. DCAR compiled the results into tables in the video, but they’re all in Chinese – so CarNewsChina helpfully compiled a table in English text form.
And as mentioned above, the Teslas each failed a different test, despite having the same systems installed. It’s possible that they could have been on different versions of FSD, but each individual update usually doesn’t make that much difference in capability.
This inconsistency doesn’t inspire confidence – given systems showed wildly differing results in the same situation, it makes one think that some of the systems might have just had a good or bad day, and that a future test could flip the results completely. The problem is, we don’t know exactly what went wrong, because we can’t examine the rules in the code that led to these decisions… because there is no ruleset behind the machine learning models used by ADAS systems these days.
In the video, DCAR interviewed Lu Guang Quan, from the Beijing University of Aeronautics and Astronautics, who pointed out this behavior as a concern with today’s ADAS systems. Since so many of them use machine learning to learn the rules of driving, when mistakes happen it’s impossible to figure out what rule in the computer’s programming might have led to the error.
“A learning model is just collecting experience. It knows how to drive but not why,” said Lu. “These so-called ‘long tail scenarios’ barely ever happen, but the risk is sky high. You won’t find them in any training dataset. The systems straight up haven’t learned this stuff.”
Lu said that “rule based models would provide stronger failsafes,” because then it would be possible to correct errors in the code, rather than the black box that machine learning models currently offer.
Given the results of its tests, DCAR concludes the video by saying “We hope everyone takes a rational look at this. These highway crash recreations show the limits of ADAS. Given their current capabilities, they cannot support full hands-free or feet-free driving. No matter what marketing claims, we should treat ADAS only as a safety assist. Human driving must remain primary. ADAS only helps reduce your driving fatigue. That 1% risk, once it happens, it can lead to 100% casualties.”
So we at Electrek also hope this is a reminder to everyone who has gotten comfortable with using these systems routinely. Not only is there still a lot they can’t do, but even if your car does show it’s capable of handling a situation once, there’s always a chance it might do something different the next time around. So keep your eyes on the road – and don’t just leave it to God’s Eye to watch what’s going on.
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Environment
Under Trump, Uncle Sam is becoming an active investor at a scale not seen outside war or major crises
The Trump administration has taken direct stakes in companies on a scale rarely seen in the U.S. outside wartime or economic crisis, pushing a Republican Party that traditionally championed free-market capitalism to embrace state intervention in industries viewed as important for national security.
Japan’s Nippon Steel agreed to give President Donald Trump a “golden share” in U.S. Steel as a condition for the two companies’ controversial merger. Trump now personally wields sweeping veto power over major business decisions made by the nation’s third-largest steel producer.
“You know who has the golden share? I do,” Trump said at a summit on artificial intelligence and energy in Pittsburgh on July 15.
The president’s golden share in U.S. Steel is similar to nationalizing a company but without any of the benefits that a company normally receives, such as direct investment by the government, said Sarah Bauerle Danzman, an expert on foreign investment and national security at the Atlantic Council, a think tank focused on international affairs.
But the Trump administration demonstrated earlier this month that it is also willing to buy directly into publicly traded corporations. The Department of Defense agreed to purchase a $400 million equity stake in rare-earth miner MP Materials, making the Pentagon the company’s largest shareholder.
This level of support by the federal government for a mining company is unprecedented, said Gracelin Baskaran, an expert on critical minerals at the Center for Strategic and International Studies.
“This is the biggest public-private cooperation that the mining industry has ever had here in the United States,” Baskaran said. “Historically, DOD has never done equity in a mining company or a mining project.”
Trump’s unique hold over the Republican Party gives him the ability to intervene in companies on a scale that would be difficult politically for a Democratic president, Danzman said.
“The Democrat would have been accused of being a communist and a lot of other Republicans probably would not have felt comfortable moving in this particular direction because of their greater commitment to market principles,” Danzman said. Trump is expanding the range of what is possible in the U.S. in terms of state intervention in markets, she said.
The White House did not immediately respond to a request for comment.
More state investments likely
More interventions could be on the horizon as the Trump administration develops a policy to support U.S. companies in strategic industries against state-backed competition from China.
Interior Secretary Doug Burgum said in April that the U.S. government might need to make an “equity investment in each of these companies that’s taking on China in critical minerals.” The Pentagon’s investment in MP Materials is a model for future public-private partnerships, CEO James Litinsky said.
“It’s a new way forward to accelerate free markets, to get the supply chain on shore that we want,” Litinsky told CNBC. The U.S. government is helping the mining industry fight “Chinese mercantilism,” the CEO said.
Meanwhile, the golden share in U.S. Steel is a potential model for foreign direct investment “transactions that really affect our national security but where it’s going to be great for our economic growth,” Sen. Dave McCormick, R-Pa., said in a May interview with CNBC.
“Having taken a stake in US Steel and MP, we’re now left to wonder where this administration will find its next investment,” Don Bilson, an analyst at Gordon Haskett, wrote in a note to clients earlier this month.
Trump proposed in January that the U.S. should take a 50% stake in social media app TikTok as part of a joint venture. China’s ByteDance is required under a recently passed law to divest TikTok or the platform will be banned in the U.S. Trump extended ByteDance’s compliance deadline until Sept. 17.
Past precedent
The U.S. has a long history of intervening in industries, particularly where national defense is concerned, said Mark Wilson, a historian at the University of North Carolina, Charlotte, who studies the military-industrial complex.
But past interventions were often temporary and typically happened during war, economic crisis or took the form of bailouts to prevent a major player in a critical industry from going bankrupt.
The U.S. government bought a majority stake in General Motors to prevent the automaker from collapsing in the wake of the 2008 financial crisis, ultimately selling off its shares at a loss to the taxpayer. In the 1970s, defense giant Lockheed and automaker Chrysler received government bailouts.
During World War I, President Woodrow Wilson nationalized the railroads, but he returned them to private ownership after the conflict. The Roosevelt administration made sweeping interventions during the Great Depression and World War II, from establishing the Tennessee Valley Authority to making big investments in the nation’s manufacturing capacity.
China looms large
The U.S. is not fighting an economic crisis or war today, but the return of great power competition with Russia and China and the supply chain disruptions of the Covid-19 pandemic have led to more nationalistic economic policies, said UNC’s Wilson.
The U.S. has increasingly recognized that China’s economic model is based on manufacturing overcapacity that dumps products “onto global markets in ways that make it hard for other markets to compete,” Danzman said.
The threat posed by China’s dominance of the rare-earth supply chain became apparent in April when Beijing imposed export restrictions against the U.S., Baskaran said. Within weeks, automakers warned they would have to halt production due to a rare-earth shortage, forcing the U.S. back to the negotiating table with Beijing, she said.
“The historical moment we’re in does seem to be one where there is this reassessment of assumptions of the previous generation about the efficacy of markets and free trade to solve all our problems in national security,” Wilson said.
The question is whether state intervention can solve the failure of the free market to address national security concerns in industries like rare earths, Danzman said.
“When you step in to try to address one of these market failures with this kind of government intervention, you can have a cascade of new market failures,” she said. “You’re distorting the market more.”
Lucid’s electric minivan can outsprint the Chevy Corvette Z06, and it has more interior space than a Ford Explorer. Is the Lucid Gravity really the “ultimate uncompromising SUV?”
Lucid Gravity SUV is faster than a Corvette Z06
Lucid’s electric SUV is impressive inside and out. The Gravity provides up to 450 miles of driving range, ultra-fast charging (200 miles in under 11 mins), and it even offers up to 120 cubic feet of cargo space. That’s more than the Ford Explorer (87.8 cu ft).
It’s also faster than most sports cars. The Grand Touring trim has up to 845 hp, good for a 0 to 60 mph sprint in just 3.4 seconds, but the Dream Edition takes it to another level.
Powered by dual electric motors, the Lucid Gravity Dream Edition boasts 1,070 hp. To see how Lucid’s minivan stacks up against the competition, Car and Driver nabbed one for testing.
On the test track, the Lucid’s minivan covered a quarter-mile in just 10.6 secs, beating a Chevrolet Corvette Z06 to 150 mph by nearly three seconds.
According to Car and Driver, the Gravity didn’t just impress in the quarter-mile, “it was a beast in every acceleration metric.” Lucid’s SUV hit 30 mph in 1.4 seconds, 70 mph in 3.7 secs, and topped 100 mph in just 5.9 seconds.
Dave Vanderwerp, the testing director who took the Gravity for a spin, said the electric SUV “gets a sort of second wave of thrust starting around 60 mph.”
With a quarter-mile of just 10.6 secs, Lucid’s Gravity is the fastest SUV they have ever tested, beating out the Rivian Tri-Motor Max (11.1 secs), BMW iX M60 (11.5 secs), and Mercedes-AMG EQE53 SUV.
Although the Rivian’s 850 hp R1S Tri-Motor beat the Gravity to 60 mph, Lucid’s SUV sprinted ahead in the quarter-mile, traveling nearly 20 mph faster.
It was also faster than gas-powered super SUVs, including the Lamborghini Urus Performante (11.2 secs) and Porsche Cayenne Turbo GT (11.2 secs). However, they have yet to test a Tesla Model X Plaid, so that could change the game.
In what it called the “1,000 hp mom missiles” drag race, Hagerty recently pitted the Gravity Dream Edition against the Audi RS Q8 Performance, Range Rover Sport SV, Porsche Macan Turbo Electric, Rivian R1S Quad, and Porsche Panamera Turbo S E-Hybrid.
The result was a three-way tie between Lucid’s Gravity, the Porsche Panamera Turbo, and Rivian R1S Quad hitting the quarter-mile in 10.5 seconds.
The Lucid Gravity is available to order starting at $94,900 in the US. Later this year, Lucid is launching the lower-priced Touring trim, priced from $79,900.
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