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Scientists use generative AI to answer complex questions in physics

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Scientists use generative AI to answer complex questions in physics


When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.

To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.

Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.

Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.

Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.

“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.

Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.

Detecting phase transitions using AI

While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.

These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.

In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.

More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.

The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.

The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.

Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).

However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.

A more knowledgeable model

The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.

“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.

This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.

This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.

At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”

Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.

In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.

This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.



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New drug shows promise in clearing HIV from brain

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Scientists use generative AI to answer complex questions in physics


An experimental drug originally developed to treat cancer may help clear HIV from infected cells in the brain, according to a new Tulane University study.

For the first time, researchers at Tulane National Primate Research Center found that a cancer drug significantly reduced levels of SIV, the nonhuman primate equivalent of HIV, in the brain by targeting and depleting certain immune cells that harbor the virus.

Published in the journal Brain, this discovery marks a significant step toward eliminating HIV from hard-to-reach reservoirs where the virus evades otherwise effective treatment.

“This research is an important step in tackling brain-related issues caused by HIV, which still affect people even when they are on effective HIV medication,” said lead study author Woong-Ki Kim, PhD, associate director for research at Tulane National Primate Research Center. “By specifically targeting the infected cells in the brain, we may be able to clear the virus from these hidden areas, which has been a major challenge in HIV treatment.”

Antiretroviral therapy (ART) is an essential component of successful HIV treatment, maintaining the virus at undetectable levels in the blood and transforming HIV from a terminal illness into a manageable condition. However, ART does not completely eradicate HIV, necessitating lifelong treatment. The virus persists in “viral reservoirs” in the brain, liver, and lymph nodes, where it remains out of reach of ART.

The brain has been a particularly challenging area for treatment due to the blood-brain barrier — a protective membrane that shields it from harmful substances but also blocks treatments, allowing the virus to persist. In addition, cells in the brain known as macrophages are extremely long-lived, making them difficult to eradicate once they become infected.

Infection of macrophages is thought to contribute to neurocognitive dysfunction, experienced by nearly half of those living with HIV. Eradicating the virus from the brain is critical for comprehensive HIV treatment and could significantly improve the quality of life for those with HIV-related neurocognitive problems.

Researchers focused on macrophages, a type of white blood cell that harbors HIV in the brain. By using a small molecule inhibitor to block a receptor that increases in HIV-infected macrophages, the team successfully reduced the viral load in the brain. This approach essentially cleared the virus from brain tissue, providing a potential new treatment avenue for HIV.

The small molecule inhibitor used, BLZ945, has previously been studied for therapeutic use in amyotrophic lateral sclerosis (ALS) and brain cancer, but never before in the context of clearing HIV from the brain.

The study, which took place at the Tulane National Primate Research Center, utilized three groups to model human HIV infection and treatment: an untreated control group, and two groups treated with either a low or high dose of the small molecule inhibitor for 30 days. The high-dose treatment lead to a notable reduction in cells expressing HIV receptor sites, as well as a 95-99% decrease in viral DNA loads in the brain .

In addition to reducing viral loads, the treatment did not significantly impact microglia, the brain’s resident immune cells, which are essential for maintaining a healthy neuroimmune environment. It also did not show signs of liver toxicity at the doses tested.

The next step for the research team is to test this therapy in conjunction with ART to assess its efficacy in a combined treatment approach. This could pave the way for more comprehensive strategies to eradicate HIV from the body entirely.

This research was funded by the National Institutes of Health, including grants from the National Institute of Mental Health and the National Institute of Neurological Disorders and Stroke, and was supported with resources from the Tulane National Primate Research Center base grant of the National Institutes of Health, P51 OD011104.



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Chemical analyses find hidden elements from renaissance astronomer Tycho Brahe’s alchemy laboratory

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In the Middle Ages, alchemists were notoriously secretive and didn’t share their knowledge with others. Danish Tycho Brahe was no exception. Consequently, we don’t know precisely what he did in the alchemical laboratory located beneath his combined residence and observatory, Uraniborg, on the now Swedish island of Ven.

Only a few of his alchemical recipes have survived, and today, there are very few remnants of his laboratory. Uraniborg was demolished after his death in 1601, and the building materials were scattered for reuse.

However, during an excavation in 1988-1990, some pottery and glass shards were found in Uraniborg’s old garden. These shards were believed to originate from the basement’s alchemical laboratory. Five of these shards — four glass and one ceramic — have now undergone chemical analyses to determine which elements the original glass and ceramic containers came into contact with.

The chemical analyses were conducted by Professor Emeritus and expert in archaeometry, Kaare Lund Rasmussen from the Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark. Senior researcher and museum curator Poul Grinder-Hansen from the National Museum of Denmark oversaw the insertion of the analyses into historical context.

Enriched levels of trace elements were found on four of them, while one glass shard showed no specific enrichments. The study has been published in the journal Heritage Science.

“Most intriguing are the elements found in higher concentrations than expected — indicating enrichment and providing insight into the substances used in Tycho Brahe’s alchemical laboratory,” said Kaare Lund Rasmussen.

The enriched elements are nickel, copper, zinc, tin, antimony, tungsten, gold, mercury, and lead, and they have been found on either the inside or outside of the shards.

Most of them are not surprising for an alchemist’s laboratory. Gold and mercury were — at least among the upper echelons of society — commonly known and used against a wide range of diseases.

“But tungsten is very mysterious. Tungsten had not even been described at that time, so what should we infer from its presence on a shard from Tycho Brahe’s alchemy workshop?,” said Kaare Lund Rasmussen.

Tungsten was first described and produced in pure form more than 180 years later by the Swedish chemist Carl Wilhelm Scheele. Tungsten occurs naturally in certain minerals, and perhaps the element found its way to Tycho Brahe’s laboratory through one of these minerals. In the laboratory, the mineral might have undergone some processing that separated the tungsten, without Tycho Brahe ever realizing it.

However, there is also another possibility that Professor Kaare Lund Rasmussen emphasizes has no evidence whatsoever — but which could be plausible.

Already in the first half of the 1500s, the German mineralogist Georgius Agricola described something strange in tin ore from Saxony, which caused problems when he tried to smelt tin. Agricola called this strange substance in the tin ore “Wolfram” (German for Wolf’s froth, later renamed to tungsten in English).

“Maybe Tycho Brahe had heard about this and thus knew of tungsten’s existence. But this is not something we know or can say based on the analyses I have done. It is merely a possible theoretical explanation for why we find tungsten in the samples,” said Kaare Lund Rasmussen.

Tycho Brahe belonged to the branch of alchemists who, inspired by the German physician Paracelsus, tried to develop medicine for various diseases of the time: plague, syphilis, leprosy, fever, stomach aches, etc. But he distanced himself from the branch that tried to create gold from less valuable minerals and metals.

In line with the other medical alchemists of the time, he kept his recipes close to his chest and shared them only with a few selected individuals, such as his patron, Emperor Rudolph II, who allegedly received Tycho Brahe’s prescriptions for plague medicine.

We know that Tycho Brahe’s plague medicine was complicated to produce. It contained theriac, which was one of the standard remedies for almost everything at the time and could have up to 60 ingredients, including snake flesh and opium. It also contained copper or iron vitriol (sulphates), various oils, and herbs.

After various filtrations and distillations, the first of Brahe’s three recipes against plague was obtained. This could be made even more potent by adding tinctures of, for example, coral, sapphires, hyacinths, or potable gold.

“It may seem strange that Tycho Brahe was involved in both astronomy and alchemy, but when one understands his worldview, it makes sense. He believed that there were obvious connections between the heavenly bodies, earthly substances, and the body’s organs. Thus, the Sun, gold, and the heart were connected, and the same applied to the Moon, silver, and the brain; Jupiter, tin, and the liver; Venus, copper, and the kidneys; Saturn, lead, and the spleen; Mars, iron, and the gallbladder; and Mercury, mercury, and the lungs. Minerals and gemstones could also be linked to this system, so emeralds, for example, belonged to Mercury,” explained Poul Grinder-Hansen.

Kaare Lund Rasmussen has previously analyzed hair and bones from Tycho Brahe and found, among other elements, gold. This could indicate that Tycho Brahe himself had taken medicine that contained potable gold.



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Nitrogen emissions have a net cooling effect: But researchers warn against a climate solution

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Scientists use generative AI to answer complex questions in physics


An international team of researchers has found that nitrogen emissions from fertilisers and fossil fuels have a net cooling effect on the climate. But they warn increasing atmospheric nitrogen has further damaging effects on the environment, calling for an urgent reduction in greenhouse gas emissions to halt global warming.

Published today in Nature, the paper found that reactive nitrogen released in the environment through human activities cools the climate by minus 0.34 watts per square metre. While global warming would have advanced further without the input of human-generated nitrogen, the amount would not offset the level of greenhouse gasses heating the atmosphere.

The paper was led by the Max Planck Institute in Germany and included authors from the University of Sydney. It comes one day after new data from the European Union’s Copernicus Climate Change Service indicated that Sunday, 21 July was the hottest day recorded in recent history.

The net cooling effect occurs in four ways:

  • Short-lived nitrogen oxides produced by the combustion of fossil fuels pollute the atmosphere by forming fine suspended particles which shield sunlight, in turn cooling the climate;

  • ammonia (a nitrogen and hydrogen-based compound) released into the atmosphere from the application of manure and artificial fertilisers has a similar effect;

  • nitrogen applied to crops allows plants to grow more abundantly, absorbing more CO2 from the atmosphere, enabling a cooling effect;

  • nitrogen oxides also play a role in the breakdown of atmospheric methane, a potent greenhouse gas.

The researchers warned that increasing atmospheric nitrogen was not a solution for combatting climate change.

“Nitrogen fertilisers pollute water and nitrogen oxides from fossil fuels pollute the air. Therefore, increasing rates of nitrogen in the atmosphere to combat climate change is not an acceptable compromise, nor is it a solution,” said Professor Federico Maggi from the University of Sydney’s School of Civil Engineering.

Sönke Zaehle from the Max Planck Institute said: “This may sound like good news, but you have to bear in mind that nitrogen emissions have many harmful effects, for example on health, biodiversity and the ozone layer. The current findings, therefore, are no reason to gloss over the harmful effects, let alone see additional nitrogen input as a means of combatting global warming.”

Elemental nitrogen, which makes up around 78 percent of the air, is climate-neutral, but other reactive nitrogen compounds can have direct or indirect effects on the global climate — sometimes warming and at other times cooling. Nitrous oxide (N2O) is an almost 300 times more potent greenhouse gas than CO2. Other forms of nitrogen stimulate the formation of ozone in the troposphere, which is a potent greenhouse gas and enhances global warming.

Professor Maggi said the research was important as it helped the team gain an understanding of the net-effect of the distribution of nitrogen emissions from agriculture.

“This work is an extraordinary example of how complex interactions at planetary scales cannot be captured with simplistic assessment tools. It shows the importance of developing mathematical models that can show the emergence of nonlinear — or unproportional — effects across soil, land, and atmosphere,” he said.

“Even if it appears counter-intuitive, reactive nitrogen introduced in the environment, mostly as agricultural fertilisers, can reduce total warming. However, this is minor compared with the reduction in greenhouse gas emissions required to keep the planet within safe and just operational boundaries.

“New generation computational tools are helping drive new learnings in climate change science, but understanding is not enough — we must act with great urgency to reduce greenhouse gas emissions.”

Gaining a holistic understanding of the impacts of nitrogen

The scientists determined the overall impact of nitrogen from human sources by first analysing the quantities of the various nitrogen compounds that end up in soil, water and air.

They then fed this data into models that depict the global nitrogen cycle and the effects on the carbon cycle, for example the stimulation of plant growth and ultimately the CO2 and methane content of the atmosphere. From the results of these simulations, they used another atmospheric chemistry model to calculate the effect of man-made nitrogen emissions on radiative forcing, that is the radiant energy that hits one square metre of the Earth’s surface per unit of time.



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