Aurelia Butler

Long before researchers discovered the electron and its role in generating electrical current, they knew about electricity and were exploring its potential. One thing they learned early on was that metals were great conductors of both electricity and heat.
And in 1853, two scientists showed that those two admirable properties of metals were somehow related: At any given temperature, the ratio of electronic conductivity to thermal conductivity was roughly the same in any metal they tested. This so-called Wiedemann-Franz law has held ever since — except in quantum materials, where electrons stop behaving as individual particles and glom together into a sort of electron soup. Experimental measurements have indicated that the 170-year-old law breaks down in these quantum materials, and by quite a bit.
Now, a theoretical argument put forth by physicists at the Department of Energy’s SLAC National Accelerator Laboratory, Stanford University and the University of Illinois suggests that the law should, in fact, approximately hold for one type of quantum material — the copper oxide superconductors, or cuprates, which conduct electricity with no loss at relatively high temperatures.
In a paper published in Science today, they propose that the Wiedemann-Franz law should still roughly hold if one considers only the electrons in cuprates. They suggest that other factors, such as vibrations in the material’s atomic latticework, must account for experimental results that make it look like the law does not apply.
This surprising result is important to understanding unconventional superconductors and other quantum materials, said Wen Wang, lead author of the paper and a PhD student with the Stanford Institute for Materials and Energy Sciences (SIMES) at SLAC.
“The original law was developed for materials where electrons interact with each other weakly and behave like little balls that bounce off defects in the material’s lattice,” Wang said. “We wanted to test the law theoretically in systems where neither of these things was true.”
Peeling a quantum onion
Superconducting materials, which carry electric current without resistance, were discovered in 1911. But they operated at such extremely low temperatures that their usefulness was quite limited.

That changed in 1986, when the first family of so-called high-temperature or unconventional superconductors — the cuprates — was discovered. Although cuprates still require extremely cold conditions to work their magic, their discovery raised hopes that superconductors could someday work at much closer to room temperature — making revolutionary technologies like no-loss power lines possible.
After nearly four decades of research, that goal is still elusive, although a lot of progress has been made in understanding the conditions in which superconducting states flip in and out of existence.
Theoretical studies, performed with the help of powerful supercomputers, have been essential for interpreting the results of experiments on these materials and for understanding and predicting phenomena that are out of experimental reach.
For this study, the SIMES team ran simulations based on what’s known as the Hubbard model, which has become an essential tool for simulating and describing systems where electrons stop acting independently and join forces to produce unexpected phenomena.
The results show that when you only take electron transport into account, the ratio of electronic conductivity to thermal conductivity approaches what the Wiedemann-Franz law predicts, Wang said. “So, the discrepancies that have been seen in experiments should be coming from other things like phonons, or lattice vibrations, that are not in the Hubbard model,” she said.
SIMES staff scientist and paper co-author Brian Moritz said that although the study did not investigate how vibrations cause the discrepancies, “somehow the system still knows that there is this correspondence between charge and heat transport amongst the electrons. That was the most surprising result.”
From here, he added, “maybe we can peel the onion to understand a little bit more.”
Major funding for this study came from the DOE Office of Science. Computational work was carried out at Stanford University and on resources of the National Energy Research Scientific Computing Center, which is a DOE Office of Science user facility. More

Autonomous vehicles hold the promise of tackling traffic congestion, enhancing traffic flow through vehicle-to-vehicle communication, and revolutionizing the travel experience by offering comfortable and safe journeys. Additionally, integrating autonomous driving technology into electric vehicles could contribute to more eco-friendly transportation solutions.
A critical requirement for the success of autonomous vehicles is their ability to detect and navigate around obstacles, pedestrians, and other vehicles across diverse environments. Current autonomous vehicles employ smart sensors such as LiDARs (Light Detection and Ranging) for a 3D view of the surroundings and depth information, RADaR (Radio Detection and Ranging) for detecting objects at night and cloudy weather, and a set of cameras for providing RGB images and a 360-degree view, collectively forming a comprehensive dataset known as point cloud. However, these sensors often face challenges like reduced detection capabilities in adverse weather, on unstructured roads, or due to occlusion.
To overcome these shortcomings, an international team of researchers led by Professor Gwanggil Jeon from the Department of Embedded Systems Engineering at Incheon National University (INU), Korea, has recently developed a groundbreaking Internet-of-Things-enabled deep learning-based end-to-end 3D object detection system. “Our proposed system operates in real time, enhancing the object detection capabilities of autonomous vehicles, making navigation through traffic smoother and safer,” explains Prof. Jeon. Their paper was made availableonline on October 17, 2022, and published in Volume 24, Issue 11 of the journal IEEE Transactions on Intelligent Transport Systems on November 2023.
The proposed innovative system is built on the YOLOv3 (You Only Look Once) deep learning object detection technique, which is the most active state-of-the-art technique available for 2D visual detection. The researchers first used this new model for 2D object detection and then modified the YOLOv3 technique to detect 3D objects. Using both point cloud data and RGB images as input, the system generates bounding boxes with confidence scores and labels for visible obstacles as output.
To assess the system’s performance, the team conducted experiments using the Lyft dataset, which consisted of road information captured from 20 autonomous vehicles traveling a predetermined route in Palo Alto, California, over a four-month period. The results demonstrated that YOLOv3 exhibits high accuracy, surpassing other state-of-the-art architectures. Notably, the overall accuracy for 2D and 3D object detection were an impressive 96% and 97%, respectively.
Prof. Jeon emphasizes the potential impact of this enhanced detection capability: “By improving detection capabilities, this system could propel autonomous vehicles into the mainstream. The introduction of autonomous vehicles has the potential to transform the transportation and logistics industry, offering economic benefits through reduced dependence on human drivers and the introduction of more efficient transportation methods.”
Furthermore, the present work is expected to drive research and development in various technological fields such as sensors, robotics, and artificial intelligence. Going ahead, the team aims to explore additional deep learning algorithms for 3D object detection, recognizing the current focus on 2D image development.
In summary, this groundbreaking study could pave the way for a widespread adoption of autonomous vehicles and, in turn, a more environment-friendly and comfortable mode of transport. More

Hung from a common utility pole, a fiber optic cable — the kind bringing high-speed internet to more and more American households — can be turned into a sensor to detect temperature changes, vibrations, and even sound, through an emerging technology called distributed fiber optic sensing.
However, as NEC Labs America photonics researcher Sarper Ozharar, Ph.D., explains, acoustic sensing in fiber optic cables “is limited to only nearby sound sources or very loud events, such as emergency vehicles, car alarms, or cicada emergences.”
Cicadas? Indeed, periodical cicadas — the insects known for emerging by the billions on 13- or 17-year cycles and making a collective racket with their buzzy mating calls — are loud enough to be detected through fiber optic acoustic sensing. And a new proof-of-concept study shows how the technology could open new pathways for charting the populations of these famously ephemeral bugs.
“I was surprised and excited to learn how much information about the calls was gathered, despite it being located near a busy section of Middlesex County in New Jersey,” says entomologist Jessica Ware, Ph.D., associate curator and chair of the Division of Invertebrate Zoology at the American Museum of Natural History and co-author on the study, published in the Entomological Society of America’s Journal of Insect Science.
As the researchers explain in their report, distributed fiber optic sensing is based on detecting and analyzing “backscatter” in a cable. When an optical pulse is sent through a fiber cable, tiny imperfections or disturbances in the cable cause a small fraction of the signal to bounce back to the source. Timing the arrival of the backscattered light can be used to calculate the exact point along the cable from which it bounced back. And, monitoring how the backscatter varies over time creates a signature of the disturbance — which, in the case of acoustic sensing, can indicate volume and frequency of the sound.
A single sensor can be deployed on a huge segment of cable, too; the researchers offer an example of a 50-kilometer cable with a sensor that can detect the location of disturbances at a scale as precise as 1 meter. “This is identical to installing 50,000 [acoustic] sensors in the monitored region that are inherently synchronized and do not require onsite power supply,” they write.
In 2021, Brood X, the largest of several populations of cicadas that emerge on 17-year cycles, came out of the ground in at least 15 states and the District of Columbia in the Midwest and mid-Atlantic regions of the U.S., including New Jersey, where Ozharar works at NEC Laboratories America, Inc. There, Ozharar and colleagues used NEC’s fiber-sensing test apparatus — cable strung on three 35-foot utility poles on the grounds of NEC’s lab in Princeton — to see if they could detect and analyze the sound of Brood X cicadas buzzing in trees nearby between June 9 and June 24 that year.

Sure enough, the cicadas’ buzzing was evident. It showed up as a strong signal at 1.33 kilohertz (kHz) via the fiber optic sensing, which matched the frequency of the cicadas’ call measured with a traditional audio sensor placed in same location. The researchers also observed the cicadas’ peak frequency varying between 1.2 kHz and 1.5 kHz, a pattern that appeared to follow changes in temperature at the test site. The overall intensity of the cicadas’ buzzing was also observed through the fiber optic sensing, and the signal decreased over the course of the test period, as the cicadas’ chorus peaked and then faded as they reached the end of their reproductive period.
“We think it is really exciting and interesting that this new technology, designed and optimized for other applications and seemingly unrelated to entomology, can support entomological studies,” Ozharar says. Indeed, fiber optic sensors are multifunctional, meaning they can be installed and used for any number of purposes, detecting cicadas one day and some other disturbance the next.
Ware says fiber optic sensing could soon play a role in detecting a variety of insects. “Periodical cicadas were a noisy cohort that was picked up by these systems, but it will be interesting to see if annual measurements of insect soundscapes and vibrations could be useful in monitoring insect abundance in an area across seasons and years,” she says.
As for periodical cicadas, more than a dozen broods are known to emerge in different years and different areas of the eastern United States. The growing network of fiber optic infrastructure in the country — with fiber internet available to more than 40 percent of U.S. households as of 2022, according to the Fiber Broadband Association — could be incorporated into entomologists’ efforts to observe and measure these emergences over time.
“Thanks to the booming development of broadband access and telecommunications, fiber cables are ubiquitously available across communities, weaving a vast network that not only provides high-speed internet but also serves as a foundation for the next generation of sensing technologies,” Ozharar says.
Brood X cicadas will remain underground until 2038. Their brief appearances and massive numbers make them a challenge to study, but the long gap between their arrivals allows entomologists to make significant technological leaps in the interim. In 2021, Brood X was observed in unprecedented volume through a crowdsourced mobile smartphone app — a method barely conceivable when Brood X had last emerged in 2004. By 2038, fiber optic sensing could well be the next avenue leading to a similar advance. More

Researchers have developed an AI model that can predict where a drug molecule can be chemically altered.
A team of researchers from LMU, ETH Zurich, and Roche Pharma Research and Early Development (pRED) Basel has used artificial intelligence (AI) to develop an innovative method that predicts the optimal method for synthesizing drug molecules. “This method has the potential to significantly reduce the number of required lab experiments, thereby increasing both the efficiency and sustainability of chemical synthesis,” says David Nippa, lead author of the corresponding paper, which has been published in the journal Nature Chemistry. Nippa is a doctoral student in Dr. David Konrad’s research group at the Faculty of Chemistry and Pharmacy at LMU and at Roche.
Active pharmaceutical ingredients typically consist of a framework to which functional groups are attached. These groups enable a specific biological function. To achieve new or improved medical effects, functional groups are altered and added to new positions in the framework. However, this process is particularly challenging in chemistry, as the frameworks, which mainly consist of carbon and hydrogen atoms, are hardly reactive themselves. One method of activating the framework is the so-called borylation reaction. In this process, a chemical group containing the element boron is attached to a carbon atom of the framework. This boron group can then be replaced by a variety of medically effective groups. Although borylation has great potential, it is difficult to control in the lab.
Together with Kenneth Atz, a doctoral student at ETH Zurich, David Nippa developed an AI model that was trained on data from trustworthy scientific works and experiments from an automated lab at Roche. It can successfully predict the position of borylation for any molecule and provides the optimal conditions for the chemical transformation. “Interestingly, the predictions improved when the three-dimensional information of the starting materials were taken into account, not just their two-dimensional chemical formulas,” says Atz.
The method has already been successfully used to identify positions in existing active ingredients where additional active groups can be introduced. This helps researchers develop new and more effective variants of known drug active ingredients more quickly. More

Noise on the radio when reception is poor is a typical example of how fluctuations mask a physical signal. In fact, such interference or noise occurs in every physical measurement in addition to the actual signal. “Even in the loneliest place in the universe, where there should be nothing at all, there are still fluctuations of the electromagnetic field,” says physicist Ulrich Nowak. In the Collaborative Research Centre (CRC) 1432 “Fluctuations and Nonlinearities in Classical and Quantum Matter beyond Equilibrium” at the University of Konstanz, researchers do not see this omnipresent noise as a disturbing factor that needs to be eliminated as far as possible, but as a source of information that tells us something about the signal.
No magnetic effect, but fluctuations
This approach has now proved successful when investigating antiferromagnets. Antiferromagnets are magnetic materials in which the magnetizations of several sub-lattices cancel out each other. Nevertheless, antiferromagnetic insulators are considered promising for energy-efficient components in the field of information technology. As they have hardly any magnetic fields on the outside, they are very difficult to characterize physically. Yet, antiferromagnets are surrounded by magnetic fluctuations, which can tell us a lot about this weakly magnetic material.
In this spirit, the groups of the two materials scientists Ulrich Nowak and Sebastian Gönnenwein analysed the fluctuations of antiferromagnetic materials in the context of the CRC. The decisive factor in their theoretical as well as experimental study, recently published in the journal Nature Communications, was the specific frequency range. “We measure very fast fluctuations and have developed a method with which fluctuations can still be detected on the ultrashort time scale of femtoseconds,” says experimental physicist Sebastian Gönnenwein. A femtosecond is one millionth of a billionth of a second.
New experimental approach for ultrafast time scales
On slower time scales, one could use electronics that are fast enough to measure these fluctuations. On ultrafast time scales, this no longer works, which is why a new experimental approach had to be developed. It is based on an idea from the research group of Alfred Leitenstorfer, who is also a member of the Collaborative Research Centre. Employing laser technology, the researchers use pulse sequences or pulse pairs in order to obtain information about fluctuations. Initially, this measurement approach was developed to investigate quantum fluctuations, and has now been extended to fluctuations in magnetic systems. Takayuki Kurihara from the University of Tokyo played a key role in this development as the third cooperation partner. He was a member of the Leitenstorfer research group and the Zukunftskolleg at the University of Konstanz from 2018 to 2020.
Detection of fluctuations using ultrashort light pulses
In the experiment, two ultrashort light pulses are transmitted through the magnet with a time delay, testing the magnetic properties during the transit time of each pulse, respectively. The light pulses are then checked for similarity using sophisticated electronics. The first pulse serves as a reference, the second contains information about how much the antiferromagnet has changed in the time between the first and second pulse. Different measurement results at the two points of time confirm the fluctuations. Ulrich Nowak’s research group also modelled the experiment in elaborate computer simulations in order to better understand its results.
One unexpected result was the discovery of what is known as telegraph noise on ultrashort time scales. This means that there is not only unsorted noise, but also fluctuations in which the system switches back and forth between two well-defined states.Such fast, purely random switching has never been observed before and could be interesting for applications such as random number generators. In any case, the new methodological possibilities for analyzing fluctuations on ultrashort time scales offer great potential for further discoveries in the field of functional materials. More

Cryptocurrency mining uses a significant amount of water amid the global water crisis, and its water demand may grow further. In a commentary published November 29 in the journal Cell Reports Sustainability, financial economist Alex de Vries provides the first comprehensive estimate of Bitcoin’s water use. He warns that its sheer scale could impact drinking water if it continues to operate without constraints, especially in countries that are already battling water scarcity, including the U.S.
“Many parts of the world are experiencing droughts, and fresh water is becoming an increasing scarce resource,” says de Vries, a PhD student at Vrije Universiteit Amsterdam. “If we continue to use this valuable resource for making useless computations, I think that reality is really painful.”
Previous research on crypto’s resource use has primarily focused on electricity consumption. When mining Bitcoins, the most popular cryptocurrency, miners around the world are essentially racing to solve mathematical equations on the internet, and the winners get a share of Bitcoin’s value. In the Bitcoin network, miners make about 350 quintillion — that is, 350 followed by 18 zeros — guesses every second of the day, an activity that consumes a tremendous amount of computing power.
“The right answer emerges every 10 minutes, and the rest of the data, quintillions of them, are computations that serve no further purpose and are therefore immediately discarded,” de Vries says.
During the same process, a large amount of water is used to cool the computers at large data centers. Based on data from previous research, de Vries calculates that Bitcoin mining consumes about 8.6 to 35.1 gigaliters (GL) of water per year in the U.S. In addition to cooling computers, coal- and gas-fired power plants that provide electricity to run the computers also use water to lower the temperature. This cooling water is evaporated and not available to be reused. Water evaporated from hydropower plants also adds to the water footprint of Bitcoin’s power demand.
In total, de Vries estimates that in 2021, Bitcoin mining consumed over 1,600 GL of water worldwide. Each transaction on the Bitcoin blockchain uses 16,000 liters of water on average, about 6.2 million times more than a credit card swipe, or enough to fill a backyard swimming pool. Bitcoin’s water consumption is expected to increase to 2,300 GL in 2023, de Vries says,
In the U.S., Bitcoin mining consumes about 93 GL to 120 GL of water every year, equivalent to the average water consumption of 300,000 U.S. households or a city like Washington, D.C.

“The price of Bitcoin just increased recently and reached its highest point of the year, despite the recent collapse of several cryptocurrency platforms. This will have serious consequences, because the higher the price, the higher the environmental impact,” de Vries says. “The most painful thing about cryptocurrency mining is that it uses so much computational power and so much resources, but these resources are not going into creating some kind of model, like artificial intelligence, that you can then use for something else. It’s just making useless computations.”
At a value of more than $37,000 per coin, Bitcoin continues to expand across the world. In countries in Central Asia, where the dry climate is already putting pressure on freshwater supply, increased Bitcoin mining activities will worsen the problem. In Kazakhstan, a global cryptocurrency mining hub, Bitcoin transactions consumed 997.9 GL of water in 2021. The Central Asia country is already grappling with a water crisis, and Bitcoin mining’s growing water footprint could exacerbate the shortage.
De Vries suggests that approaches such as modifying Bitcoin mining’s software could cut down on the power and water needed for this process. Incorporating renewable energy sources that don’t involve water, including wind and solar, can also reduce water consumption.
“But do you really want to spend wind and solar power for crypto? In many countries including the U.S., the amount of renewable energy is limited. Sure you can move some of these renewable energy sources to crypto, but that means something else will be powered with fossil fuels. I’m not sure how much you gain,” he says. More

Entanglement is a quantum phenomenon where the properties of two or more particles become interconnected in such a way that one cannot assign a definite state to each individual particle anymore. Rather, we have to consider all particles at once that share a certain state. The entanglement of the particles ultimately determines the properties of a material.
“Entanglement of many particles is the feature that makes the difference,” emphasizes Christian Kokail, one of the first authors of the paper now published in Nature. “At the same time, however, it is very difficult to determine.” The researchers led by Peter Zoller at the University of Innsbruck and the Institute of Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences (ÖAW) now provide a new approach that can significantly improve the study and understanding of entanglement in quantum materials. In order to describe large quantum systems and extract information from them about the existing entanglement, one would naively need to perform an impossibly large number of measurements. “We have developed a more efficient description, that allows us to extract entanglement information from the system with drastically fewer measurements,” explains theoretical physicist Rick van Bijnen.
In an ion trap quantum simulator with 51 particles, the scientists have imitated a real material by recreating it particle by particle and studying it in a controlled laboratory environment. Very few research groups worldwide have the necessary control of so many particles as the Innsbruck experimental physicists led by Christian Roos and Rainer Blatt. “The main technical challenge we face here is how to maintain low error rates while controlling 51 ions trapped in our trap and ensuring the feasibility of individual qubit control and readout,” explains experimentalist Manoj Joshi. In the process, the scientists witnessed for the first time effects in the experiment that had previously only been described theoretically. “Here we have combined knowledge and methods that we have painstakingly worked out together over the past years. It’s impressive to see that you can do these things with the resources available today,” says an excited Christian Kokail, who recently joined the Institute for Theoretical Atomic Molecular and Optical Physics at Harvard.
Shortcut via temperature profiles
In a quantum material, particles can be more or less strongly entangled. Measurements on a strongly entangled particle yield only random results. If the results of the measurements fluctuate very much — i.e., if they are purely random — then scientists refer to this as “hot.” If the probability of a certain result increases, it is a “cold” quantum object. Only the measurement of all entangled objects reveals the exact state. In systems consisting of very many particles, the effort for the measurement increases enormously. Quantum field theory has predicted that subregions of a system of many entangled particles can be assigned a temperature profile. These profiles can be used to derive the degree of entanglement of the particles.
In the Innsbruck quantum simulator, these temperature profiles are determined via a feedback loop between a computer and the quantum system, with the computer constantly generating new profiles and comparing them with the actual measurements in the experiment. The temperature profiles obtained by the researchers show that particles that interact strongly with the environment are “hot” and those that interact little are “cold.” “This is exactly in line with expectations that entanglement is particularly large where the interaction between particles is strong,” says Christian Kokail.
Opening doors to new areas of physics
“The methods we have developed provide a powerful tool for studying large-scale entanglement in correlated quantum matter. This opens the door to the study of a new class of physical phenomena with quantum simulators that already are available today,” says quantum mastermind Peter Zoller. “With classical computers, such simulations can no longer be computed with reasonable effort.” The methods developed in Innsbruck will also be used to test new theory on such platforms.
The results have been published in Nature. Financial support for the research was provided by the Austrian Science Fund FWF, the Austrian Research Promotion Agency FFG, the European Union, the Federation of Austrian Industries Tyrol and others. More

New technology often calls for new materials — and with supercomputers and simulations, researchers don’t have to wade through inefficient guesswork to invent them from scratch.
The Materials Project, an open-access database founded at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) in 2011, computes the properties of both known and predicted materials. Researchers can focus on promising materials for future technologies — think lighter alloys that improve fuel economy in cars, more efficient solar cells to boost renewable energy, or faster transistors for the next generation of computers.
Now, Google DeepMind — Google’s artificial intelligence lab — is contributing nearly 400,000 new compounds to the Materials Project, expanding the amount of information researchers can draw upon. The dataset includes how the atoms of a material are arranged (the crystal structure) and how stable it is (formation energy).
“We have to create new materials if we are going to address the global environmental and climate challenges,” said Kristin Persson, the founder and director of the Materials Project at Berkeley Lab and a professor at UC Berkeley. “With innovation in materials, we can potentially develop recyclable plastics, harness waste energy, make better batteries, and build cheaper solar panels that last longer, among many other things.”
To generate the new data, Google DeepMind developed a deep learning tool called Graph Networks for Materials Exploration, or GNoME. Researchers trained GNoME using workflows and data that were developed over a decade by the Materials Project, and improved the GNoME algorithm through active learning. GNoME researchers ultimately produced 2.2 million crystal structures, including 380,000 that they are adding to the Materials Project and predict are stable, making them potentially useful in future technologies. The new results from Google DeepMind are published today in the journal Nature.
Some of the computations from GNoME were used alongside data from the Materials Project to test A-Lab, a facility at Berkeley Lab where artificial intelligence guides robots in making new materials. A-Lab’s first paper, also published today in Nature, showed that the autonomous lab can quickly discover novel materials with minimal human input.
Over 17 days of independent operation, A-Lab successfully produced 41 new compounds out of an attempted 58 — a rate of more than two new materials per day. For comparison, it can take a human researcher months of guesswork and experimentation to create one new material, if they ever reach the desired material at all.

To make the novel compounds predicted by the Materials Project, A-Lab’s AI created new recipes by combing through scientific papers and using active learning to make adjustments. Data from the Materials Project and GNoME were used to evaluate the materials’ predicted stability.
“We had this staggering 71% success rate, and we already have a few ways to improve it,” said Gerd Ceder, the principal investigator for A-Lab and a scientist at Berkeley Lab and UC Berkeley. “We’ve shown that combining the theory and data side with automation has incredible results. We can make and test materials faster than ever before, and adding more data points to the Materials Project means we can make even smarter choices.”
The Materials Project is the most widely used open-access repository of information on inorganic materials in the world. The database holds millions of properties on hundreds of thousands of structures and molecules, information primarily processed at Berkeley Lab’s National Energy Research Science Computing Center. More than 400,000 people are registered as users of the site and, on average, more than four papers citing the Materials Project are published every day. The contribution from Google DeepMind is the biggest addition of structure-stability data from a group since the Materials Project began.
“We hope that the GNoME project will drive forward research into inorganic crystals,” said Ekin Dogus Cubuk, lead of Google DeepMind’s Materials Discovery team. “External researchers have already verified more than 736 of GNoME’s new materials through concurrent, independent physical experiments, demonstrating that our model’s discoveries can be realized in laboratories.”
The Materials Project is now processing the compounds from Google DeepMind and adding them into the online database. The new data will be freely available to researchers, and also feed into projects such as A-Lab that partner with the Materials Project.
“I’m really excited that people are using the work we’ve done to produce an unprecedented amount of materials information,” said Persson, who is also the director of Berkeley Lab’s Molecular Foundry. “This is what I set out to do with the Materials Project: To not only make the data that I produced free and available to accelerate materials design for the world, but also to teach the world what computations can do for you. They can scan large spaces for new compounds and properties more efficiently and rapidly than experiments alone can.”
By following promising leads from data in the Materials Project over the past decade, researchers have experimentally confirmed useful properties in new materials across several areas. Some show potential for use: in carbon capture (pulling carbon dioxide from the atmosphere) as photocatalysts (materials that speed up chemical reactions in response to light and could be used to break down pollutants or generate hydrogen) as thermoelectrics (materials that could help harness waste heat and turn it into electrical power) as transparent conductors (which might be useful in solar cells, touch screens, or LEDs)Of course, finding these prospective materials is only one of many steps to solving some of humanity’s big technology challenges.
“Making a material is not for the faint of heart,” Persson said. “It takes a long time to take a material from computation to commercialization. It has to have the right properties, work within devices, be able to scale, and have the right cost efficiency and performance. The goal with the Materials Project and facilities like A-Lab is to harness data, enable data-driven exploration, and ultimately give companies more viable shots on goal.” More