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Casa Batll introduces Sofia Crespos structures of being

Casa Batll, the UNESCO World Heritage Site designed by renowned architect Antoni Gaud, introduces Sofia Crespo as its upcoming artist in residence, who will transform the iconic landmark with her Structures of Being projection mapping artwork. This immersive audiovisual event, blending artificial intelligence with the organic inspirations that fueled Gauds creations, is scheduled for January 27-28, 2024.

Following in the footsteps of Refik Anadol, who animated the buildings facade with his 2023 Living Architecture event, Crespo is the second artist to participate in Casa Batlls The Heritage of Tomorrow residency. The program is dedicated to propelling Gauds legacy into the future by inviting contemporary artists to interact with his work, through commissions, restorations, and research projects.

Structures of Being by Sofia Crespo blends organic forms and artificial intelligence | all images courtesy of Casa Batll

Visual artist Sofia Crespo (find more here) is described as a pioneer in the exploration of organic life and its evolution through artificial intelligence. Her work, Structures of Being, draws inspiration from nature, an elemental theme in Gauds legacy. The show invites viewers on a journey through Crespos personal microscope, facilitating contemplation of lifes evolution through immersive encounters with the diverse materials, beings, and natural phenomena found within Casa Batll. The artwork is accompanied by Robert M. Thomas music, featuring local performers such as organist Juan de la Rubia and the string quartet Cosmos Quartet. Sofia Crespo and Robert M. Thomas have closely collaborated to offer a comprehensive work, guiding their creations with their own algorithms, but with a common goal: to connect us with the present through nature.

Casa Batll has a unique richness per square meter, and its capacity to inspire is endless for any artist, especially the facade, the public part of the House. Sofias creative universe is perfect for capturing it because it stems from a deep observation of nature, its essence, structures, and beauty, offering a personal view that combines art and innovation, just like Gaud did in his time, shares Gary Gautier, General Manager of Casa Batll.

the mapping draws inspiration from nature, a fundamental element in Gauds work

the show is a journey through Sofia Crespos personal microscope

viewers are encouraged to contemplate the evolution of life by immersing in materials, and natural phenomena present in Casa Batll

portrait of Sofia Crespo by Filipa Aurlio

project info:

name: Structures of Being artist: Sofia Crespo location: Casa Batll

myrto katsikopoulou I designboom

jan 12, 2024

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sofia crespo to animate casa batll facade with bio-inspired AI projection mapping artwork - Designboom

Computing has already accelerated scientific discovery. Now scientists say a combination of advanced AI with next-generation cloud computing is turbocharging the pace of discovery to speeds unimaginable just a few years ago.

Microsoft and the Pacific Northwest National Laboratory (PNNL) in Richland, Washington, are collaborating to demonstrate how this acceleration can benefit chemistry and materials science two scientific fields pivotal to finding energy solutions that the world needs.

Scientists at PNNL are testing a new battery material that was found in a matter of weeks, not years, as part of the collaboration with Microsoft to use to advanced AI and high-performance computing (HPC), a type of cloud-based computing that combines large numbers of computers to solve complex scientific and mathematical tasks.

As part of this effort, the Microsoft Quantum team used AI to identify around 500,000 stable materials in the space of a few days.

The new battery material came out of a collaboration using Microsofts Azure Quantum Elements to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development in just 80 hours. Most importantly, this work breaks ground for a new way of speeding up solutions for urgent sustainability, pharmaceutical and other challenges while giving a glimpse of the advances that will become possible with quantum computing.

We think theres an opportunity to do this across a number of scientific fields, says Brian Abrahamson, the chief digital officer at PNNL. Recent technology advancements have opened up the opportunity to accelerate scientific discovery.

PNNL is a U.S. Department of Energy laboratory doing research in several areas, including chemistry and materials science, and its objectives include energy security and sustainability. That made it the ideal collaborator with Microsoft to leverage advanced AI models to discover new battery material candidates.

The development of novel batteries is an incredibly important global challenge, Abrahamson says. It has been a labor-intensive process. Synthesizing and testing materials at a human scale is fundamentally limiting.

The traditional first step of materials synthesis is to read all the published studies of other materials and hypothesize how different approaches might work out. But one of the main challenges is that people publish their success stories, not their failure stories, says Vijay Murugesan, materials sciences group lead at PNNL. That means scientists rarely benefit from learning from each others failures.

The next traditional scientific step is testing the hypotheses, typically a long, iterative process. If its a failure, we go back to the drawing board again, Murugesan says. One of his previous projects at PNNL, a vanadium redox flow battery technology, required several years to solve a problem and design a new material.

The traditional method requires looking at how to improve on what has been done in the past. Another approach would be to take all the possibilities and, through elimination, find something new. Designing new materials requires a lot of calculations, and chemistry is likely to be among the first applications of quantum computing. Azure Quantum Elements offers a cloud computing system designed for chemistry and materials science research with an eye toward eventual quantum computing, and is already working on these kinds of models, tools and workflows. These models will be improved for future quantum computers, but they are already proving useful for advancing scientific discovery using traditional computers.

To evaluate its progress in the real world, the Microsoft Quantum team focused on something ubiquitous in our lives materials for batteries.

Microsoft first trained different AI systems to do sophisticated evaluations of all the workable elements and to suggest combinations. The algorithm proposed 32 million candidates like finding a needle in a haystack. Next, the AI system found all the materials that were stable. Another AI tool filtered out candidate molecules based on their reactivity, and another based on their potential to conduct energy.

The idea isnt to find every single possible needle in the hypothetical haystack, but to find most of the good ones. Microsofts AI technology whittled the 32 million candidates down to about 500,000 mostly new stable materials, then down to 800.

At every step of the simulation where I had to run a quantum chemistry calculation, instead Im calling the machine learning model. So I still get the insight and the detailed observations that come from running the simulation, but the simulation can be up to half a million times faster, says Nathan Baker, Product Leader for Azure Quantum Elements.

AI may be fast, but it isnt perfectly accurate. The next set of filters used HPC, which provides high accuracy but uses a lot of computing power. That makes it a good tool for a smaller set of candidate materials. The first HPC verification used density functional theory to calculate the energy of each material relative to all the other states it could be in. Then came molecular dynamics simulations that combined AI and HPC to analyze the movements of atoms and molecules inside each material.

This process culled the list to 150 candidates. Finally, Microsoft scientists used HPC to evaluate the practicality of each material availability, cost and such to trim the list to 23 five of which were already known.

Thanks to this AI-HPC combination, discovering the most promising material candidates took just 80 hours.

The HPC portion accounted for 10 percent of the time spent computing and that was on an already-targeted set of molecules. This intense computing is the bottleneck, even at universities and research institutions that have supercomputers, which not only are not tailored to a specific domain but also are shared, so researchers may have to wait their turn. Microsofts cloud-based AI tools relieve this situation.

Microsoft scientists used AI to do the vast majority of the winnowing, accounting for about 90 percent of the computational time spent. PNNL materials scientists then vetted the short list down to half a dozen candidate materials. Because Microsofts AI tools are trained for chemistry, not just battery systems, they can be used for any kind of materials research, and the cloud is always accessible.

We think the cloud is a tremendous resource in improving the accessibility to research communities, Abrahamson says.

Today, Microsoft supports a chemistry-specific copilot and AI tools that together act like a magnet that pulls possible needles out of the haystack, trimming the number of candidates for further exploration so scientists know where to focus. The vision we are working toward is generative materials where I can ask for list of new battery compounds with my desired attributes, Baker says.

The hands-on stage is where the project stands now. The material has been successfully synthesized and turned into prototype batteries that are functional and will undergo multiple tests in the lab. Making the material at this point, before its commercialized, is artisanal. One of the first steps is to take solid precursors of the materials and to grind them by hand with a mortar and pestle, explains Shannon Lee, a PNNL materials scientist. She then uses a hydraulic press to compact the material into a dime-shaped pellet. It goes into a vacuum tube and is heated to 450 to 650 degrees Celsius (842 to 1202 degrees Fahrenheit), transferred to a box to keep it away from oxygen or water, and then ground into a powder for analysis.

For this material, the 10-or-more-hour process is relatively quick, Lee says. Sometimes it takes a week or two weeks to make a single material.

Then hundreds of working batteries must be tested, over thousands of different charging cycles and other conditions, and later different battery shapes and sizes to realize commercial use. Murugesan dreams of the development of a digital twin for chemistry or materials, so you dont need to go to a lab and put this material together and make a battery and test it. You can say, this is my anode and this is my cathode and thats the electrolyte and this is how much voltage Im going to apply, and then it can predict how everything will work together. Even details like, after 10,000 cycles and five years of usage, the material performance will be like this.

Microsoft is already working on digital tools to speed up the other parts of the scientific process.

The lengthy traditional process is illustrated by lithium-ion batteries. Lithium got attention as a battery component in the early 1900s, but rechargeable lithium-ion batteries didnt hit the market until the 1990s.

Today, lithium-ion batteries increasingly run our world, from phones to medical devices to electric vehicles to satellites. Lithium demand is expected to rise five to ten times by 2030, according to the U.S. Department of Energy. Lithium is already relatively scarce, and thus expensive. Mining it is environmentally and geopolitically problematic. Traditional lithium-ion batteries also pose safety issues, with the potential to catch fire or explode.

Many researchers are looking for alternatives, both for lithium and for the materials used as electrolytes. Solid-state electrolytes show promise for their stability and safety.

The newly discovered material PNNL scientists are currently testing uses both lithium and sodium, as well as some other elements, thus reducing the lithium content considerably possibly by as much as 70 percent. It is still early in the process the exact chemistry is subject to optimization and might not work out when tested at larger scale, Abrahamson cautions. He points out that the story here is not about this particular battery material, but rather the speed at which a material was identified. The scientists say the exercise itself is immensely valuable, and it has revealed some surprises.

The AI-derived material is a solid-state electrolyte. Ions shuttle back and forth through the electrolyte, between the cathode and the anode, ideally with minimal resistance.

Read more from the original source:

How AI and high-performance computing are speeding up scientific discovery - Microsoft

Proteins were made in a laboratory by a completely autonomous robot.Credit: Panther Media GmbH/Alamy

A self-driving laboratory comprising robotic equipment directed by a simple artificial intelligence (AI) model successfully reengineered enzymes without any input from humans save for the occasional hardware fix.

It is cutting-edge work, says Hctor Garca Martn, a physicist and synthetic biologist at Lawrence Berkeley National Laboratory in Berkeley, California. They are fully automating the whole process of protein engineering.

Self-driving labs meld robotic equipment with machine-learning models capable of directing experiments and interpreting results to design new procedures. The hope, say researchers, is that autonomous labs will turbo-charge the scientific process and come up with solutions that humans might not have thought of on their own.

Protein engineering is an ideal task for a self-driving lab, says Philip Romero, a protein engineer at the University of WisconsinMadison who led the study1, published on 11 January in Nature Chemical Engineering. Conventional approaches tend to rely on developing an assay for a particular property say, enzyme activity and then screening vast numbers of mutated versions of the protein. So much of the field of protein engineering is monotonous, he says.

The system that Romeros team created is powered by a relatively simple machine-learning model that relates a proteins sequence to its function, and proposes sequence changes to improve function. It delivers protein sequences for testing to lab equipment that makes the protein, measures its activity and then feeds the results back to the model to guide a new round of experiments. We set and forget it, Romero says.

In the study, the researchers tasked their self-driving lab with making metabolic enzymes called glycoside hydrolases more tolerant of high temperatures. After 20 experimental rounds, each of 4 campaigns produced new versions of the enzymes that could operate at temperatures at least 12 C warmer than the proteins the autonomous lab began with.

The researchers first attempted to run their own robotic equipment, but the machines kept breaking. So they turned to a cloud-based lab in California an existing facility containing robotic equipment that can be directed remotely with computer code and set their AI model to send instructions there. The entire experiment took around 6 months, including a 2.5-month pause due to shipping delays, and each 20-round run cost around US$5,200, the researchers estimate. A human might spend up to a year doing the same work.

Increasing the sophistication of self-driving biology labs might require a new generation of hardware, because existing automated lab equipment tends to be made with a human overseer in mind, says Garca Martn. A more fundamental challenge is to create self-driving labs able to generate knowledge that can be interpreted by machines, as well as humans.

Making proteins more heat stable is relatively simple, says Huimin Zhao, a synthetic biologist at the University of Illinois UrbanaChampaign. Its not clear how easily the self-driving lab can be adapted to alter enzymes in other ways.

Romero says his team is working on applying its self-driving lab to other protein-engineering challenges. The group also wants to incorporate more-sophisticated deep-learning tools that have driven advances in protein design.

The researchers are not, however, trying to slim down the scientific workforce. Were not making humans redundant, said study co-author Jacob Rapp, a University of WisconsinMadison protein engineer, at an online seminar presenting the work. Were replacing the boring parts, so that you can focus on the interesting bits of doing your engineering work.

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'Set it and forget it': automated lab uses AI and robotics to improve proteins - Nature.com

In 2016, researchers at AI Impacts, a project that aims to improve understanding of advanced AI development, released a survey of machine learning researchers. They were asked when they expected the development of AI systems that are comparable to humans along many dimensions, as well as whether to expect good or bad results from such an achievement.

The headline finding: The median respondent gave a 5 percent chance of human-level AI leading to outcomes that were extremely bad, e.g. human extinction. That means half of researchers gave a higher estimate than 5 percent saying they considered it overwhelmingly likely that powerful AI would lead to human extinction and half gave a lower one. (The other half, obviously, believed the chance was negligible.)

If true, that would be unprecedented. In what other field do moderate, middle-of-the-road researchers claim that the development of a more powerful technology one they are directly working on has a 5 percent chance of ending human life on Earth forever?

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In 2016 before ChatGPT and AlphaFold the result seemed much likelier to be a fluke than anything else. But in the eight years since then, as AI systems have gone from nearly useless to inconveniently good at writing college-level essays, and as companies have poured billions of dollars into efforts to build a true superintelligent AI system, what once seemed like a far-fetched possibility now seems to be on the horizon.

So when AI Impacts released their follow-up survey this week, the headline result that between 37.8% and 51.4% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction didnt strike me as a fluke or a surveying error. Its probably an accurate reflection of where the field is at.

Their results challenge many of the prevailing narratives about AI extinction risk. The researchers surveyed dont subdivide neatly into doomsaying pessimists and insistent optimists. Many people, the survey found, who have high probabilities of bad outcomes also have high probabilities of good outcomes. And human extinction does seem to be a possibility that the majority of researchers take seriously: 57.8 percent of respondents said they thought extremely bad outcomes such as human extinction were at least 5 percent likely.

This visually striking figure from the paper shows how respondents think about what to expect if high-level machine intelligence is developed: Most consider both extremely good outcomes and extremely bad outcomes probable.

As for what to do about it, there experts seem to disagree even more than they do about whether theres a problem in the first place.

The 2016 AI impacts survey was immediately controversial. In 2016, barely anyone was talking about the risk of catastrophe from powerful AI. Could it really be that mainstream researchers rated it plausible? Had the researchers conducting the survey who were themselves concerned about human extinction resulting from artificial intelligence biased their results somehow?

The survey authors had systematically reached out to all researchers who published at the 2015 NIPS and ICML conferences (two of the premier venues for peer-reviewed research in machine learning, and managed to get responses from roughly a fifth of them. They asked a wide range of questions about progress in machine learning and got a wide range of answers: Really, aside from the eye-popping human extinction answers, the most notable result was how much ML experts disagreed with one another. (Which is hardly unusual in the sciences.)

But one could reasonably be skeptical. Maybe there were experts who simply hadnt thought very hard about their human extinction answer. And maybe the people who were most optimistic about AI hadnt bothered to answer the survey.

When AI Impacts reran the survey in 2022, again contacting thousands of researchers who published at top machine learning conferences, their results were about the same. The median probability of an extremely bad, e.g., human extinction outcome was 5 percent.

That median obscures some fierce disagreement. In fact, 48 percent of respondents gave at least a 10 percent chance of an extremely bad outcome, while 25 percent gave a 0 percent chance. Responding to criticism of the 2016 survey, the team asked for more detail: how likely did respondents think it was that AI would lead to human extinction or similarly permanent and severe disempowerment of the human species? Depending on how they asked the question, this got results between 5 percent and 10 percent.

In 2023, in order to reduce and measure the impact of framing effects (different answers based on how the question is phrased), many of the key questions on the survey were asked of different respondents with different framings. But again, the answers to the question about human extinction were broadly consistent in the 5-10 percent range no matter how the question was asked.

The fact the 2022 and 2023 surveys found results so similar to the 2016 result makes it hard to believe that the 2016 result was a fluke. And while in 2016 critics could correctly complain that most ML researchers had not seriously considered the issue of existential risk, by 2023 the question of whether powerful AI systems will kill us all had gone mainstream. Its hard to imagine that many peer-reviewed machine learning researchers were answering a question theyd never considered before.

I think the most reasonable reading of this survey is that ML researchers, like the rest of us, are radically unsure about whether to expect the development of powerful AI systems to be an amazing thing for the world or a catastrophic one.

Nor do they agree on what to do about it. Responses varied enormously on questions about whether slowing down AI would make good outcomes for humanity more likely. While a large majority of respondents wanted more resources and attention to go into AI safety research, many of the same respondents didnt think that working on AI alignment was unusually valuable compared to working on other open problems in machine learning.

In a situation with lots of uncertainty like about the consequences of a technology like superintelligent AI, which doesnt yet exist theres a natural tendency to want to look to experts for answers. Thats reasonable. But in a case like AI, its important to keep in mind that even the most well-regarded machine learning researchers disagree with one another and are radically uncertain about where all of us are headed.

A version of this story originally appeared in the Future Perfect newsletter. Sign up here!

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New study: Countless AI experts doesnt know what to think on AI risk - Vox.com

AI is transforming every cognitive task we perform, from writing an email to developing software. Since the dawn of civilization, scientific discovery has been the ultimate cognitive task that has made us thrive and prosper as a species. For this reason, scientific discovery has probably the highest impact and is the most exciting use case for AI. We are announcing how the Microsoft Quantum team achieved a major milestone toward that vision, using advanced AI to screen over 32 million candidates to discover and synthesize a new material that holds the potential for better batteriesthe first real-life example of many that will be achieved in a new era of scientific discovery driven by AI.

We believe that chemistry and materials science are the hero scenario for full-scale quantum computers. That led us to design and launch Azure Quantum Elements, a product built specifically to accelerate scientific discovery with the power of AI, cloud computing, and eventually, full-scale quantum computers. Our beliefs were confirmed by working with companies like Johnson Matthey, 1910 Genetics, AkzoNobel, and many others, which led to the launch of Azure Quantum Elements in June. Over the summer, we had already demonstrated a massive screening of materials candidates, but we knew that showing what might be possible is not the same thing as proving the technology could identify something new and novel that could be synthesized. We needed a real proof point and decided to start with something useful from everyday life to hyperscale data centers: battery technology.

As demonstrated in results published in August, we used novel AI models to digitally screen over 32 million potential materials and found over 500,000 stable candidates. However, identifying candidates is only the first step of scientific discovery. Finding a material among those candidates with the right properties for the task, in this case for a new solid-state battery electrolyte, is like finding a needle in a haystack. It would involve lengthy high-performance computing (HPC) calculations and costly lab experimentation that would take multiple lifespans to complete.

Today we are sharing how AI is radically transforming this process, accelerating it from years to weeks to just days. Joining forces with the Department of Energys Pacific Northwest National Laboratory (PNNL), the Azure Quantum team applied advanced AI along with expertise from PNNL to identify a new material, unknown to us and not present in nature, with potential for resource-efficient batteries. Not only that, PNNL scientists synthesized and tested this material candidate from raw material to a working prototype, demonstrating its unique properties and its potential for a sustainable energy-storage solution, using significantly less lithium than other materials announced by industry.

This is important for many reasons. Solid-state batteries are assumed to be safer than traditional liquid or gel-like lithium batteries, and they provide more energy density. Lithium is already relatively scarce, and thus expensive. Mining it is environmentally and geopolitically problematic. Creating a battery that might reduce lithium requirements by approximately 70% could have tremendous environmental, safety, and economic benefits.

This collaboration is just the beginning of an exciting new journey bringing the power of AI to nearly every aspect of scientific research. More broadly, Microsoft is putting these breakthroughs into customers hands through our Azure Quantum Elements platform. It is the combination of scientific expertise and AI that will compress the next 250 years of chemistry and materials science innovation into the next 25, transforming every industry and ultimately unlocking a new era for scientific discovery.

You can learn more about Microsofts approach that enabled this rapid scientific discovery in the following paper.

Many of the hardest problems facing society, like reversing climate change, addressing food insecurity, or solving energy crises, are related to chemistry and materials science. Weve long believed that materials discovery is a key scenario for tackling some of these issues, but time is our greatest challengethe number of possible stable materials that must be explored to find solutions is believed to surpass the number of atoms in the known universe. Thats why at Microsoft, we recently released Azure Quantum Elements. Our cloud platform brings together a new generation of AI, cloud-powered HPC, and eventually quantum computing breakthroughs to empower our partners with the right tools to drive innovation by accelerating their discovery pipeline and dramatically reducing the time to screen new candidates.

PNNL advances the frontiers of knowledge, taking on some of the worlds greatest science and technology challenges. Distinctive strengths in chemistry, Earth sciences, biology, and data science are central to its scientific discovery mission. PNNL has established leadership in developing and validating next-generation energy storage technologies. Among the most recognizable forms of portable energy storage, lithium-ion batteries remain a cornerstone of modern portable energy storage because of their high energy-storage capacity and long lifespan.

Lithium and other strategic elements used in these batteries are finite resources with limited and geographically concentrated supplies. One of the main thrusts of our work at PNNL has been identifying new materials for increased energy storage needs of the future; ones made with sustainable materials that conserve and protect the Earths limited resources.

Through this collaboration, Microsoft and PNNL harnessed AI and cloud-powered HPC to accelerate research aimed at creating new types of battery materialssuch as those that use less lithium than traditional lithium-ion batteries, while maintaining significant conductivity. These new types of batteries could benefit both the environment and consumers. Within nine months, PNNL validated this proof-of-concept, demonstrating the potential of new HPC and AI approaches to significantly accelerate the innovation cycleit would be impossible for researchers to synthesize and test the millions of materials that were evaluated by advanced AI models in less than a week.

To achieve these results, our Azure Quantum team at Microsoft combined cloud-powered HPC calculations with new AI models that estimate characteristics of materials related to energy, force, stress, electronic band gap, and mechanical properties. These models have been trained on millions of data points from materials simulations and are thus able to minimize HPC calculations and predict materials properties 1,500 times faster than traditional density functional theory (DFT) calculations.

We began with 32.6 million candidate materials, created by substituting elements in known crystal structures with a sampling of elements across a subset of the periodic table. As a first application, we filtered this set of candidates using a workflow that combined our AI models of materials with conventional HPC-based simulations.

The first stage of screeningpublished in Augustused AI models. From the initial pool of 32.6 million materials, we found 500,000 materials predicted to be stable. We used AI models to screen this pool of materials for functional properties like redox potential and band gap, further reducing the number of potential candidates to about 800. The second screening stage combined physics simulations with the AI models. Microsoft Azure HPC was used for DFT calculations to confirm the properties from AI screening. AI models have a non-zero prediction error, so the DFT validation step is used to re-compute the properties that the AI models predicted as a higher-accuracy filter. This step was followed by molecular dynamics (MD) simulations to model structural changes.

Then, our Microsoft Quantum researchers used AI-accelerated MD simulations to investigate dynamic properties like ionic diffusivity. These simulations used AI models for forces at each MD step, rather than the slower DFT-based method. This stage reduced the number of candidates to 150. Then, practical features such as novelty, mechanics, and element availability were taken into consideration to create the set of 18 top candidates.

From there, PNNLs expertise provided insights into additional screening parameters that further narrowed the final structural candidates. The researchers at PNNL then synthesized the top candidate, characterized its structure, and measured its conductivity. The new electrolyte candidate uses approximately 70% less lithium compared to existing lithium-ion batteries, by replacing some lithium with sodium, an abundant compound.

In tests across a range of temperatures, the new compound displayed viable ionic conductivity, indicating its potential as a solid-state electrolyte material. After verifying the conductivity of the sodium-lithium chemical composition, the PNNL research team demonstrated the electrolytes technical viability by building a working all-solid-state battery, which was tested at both room temperature and high temperature (~80 C).

The discovery of this new type of electrolyte material is notable not only for its potential as a sustainable energy-storage solution, but also because it demonstrates that researchers can dramatically accelerate time to results with advanced AI models. While further validation and optimization of the material is ongoing, this initial end-to-end process took less than nine months and is the first step in a promising collaboration between Microsoft and PNNL. The discovery of other materials that could increase the sustainability of energy storage is likely on the horizon.

We bring our scientific expertise to bear on picking the most promising material candidates to move forward with. In this case, we had the AI insights that pointed us to potentially fruitful territory so much faster. After Microsofts team discovered 500,000 stable materials with AI that could be used across a variety of transformative applications, we were able to modify, test, and tune the chemical composition of this new material and quickly evaluate its technical viability for a working battery, showing the promise of advanced AI to accelerate the innovation cycle.

This achievement is indicative of the coming paradigm shift in how organizations across a wide range of industries approach research and developmentorganizations can now use computational breakthroughs to accelerate scientific discovery due to the convergence of HPC and AI. While this combination will provide scale and speed for performing quantum chemistry calculations, classical computing cannot solve certain problems without sacrificing accuracy, such as those involving many highly correlated electrons. Quantum supercomputing will help increase accuracy, and Azure Quantum Elements will integrate Microsofts scaled quantum supercomputer when available.

Azure Quantum Elements includes quantum-ready tools to prepare for the fast-approaching quantum future. For example, scientists can use it to identify the active space of molecular systems and estimate the quantum computing resources needed for large active-space systems. These tools will enable the development and optimization of hybrid algorithmsthose that combine classical and scaled quantum computingso that researchers are prepared for a quantum future.

The discovery of 500,000 stable materials with AI, leading to the identification and synthesis of a new material, is just one of the many possibilities for how Azure Quantum Elements will create unprecedented opportunities. Almost all manufactured goods would benefit from innovations in the fields of chemistry and materials science, and our goal is to enable discoveries across all industries by empowering research and development (R&D) teams with a platform that every scientist can use.

Join us in exploring the potential of Azure Quantum Elements to revolutionize chemistry and materials development:

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Unlocking a new era for scientific discovery with AI: How Microsoft's AI screened over 32 million candidates to find a ... - Microsoft