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 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°C. 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.
More information: Julian Arnold et al, Mapping out phase diagrams with generative classifiers, Physical Review Letters (2024). DOI: 10.1103/PhysRevLett.132.207301. On arXiv (2023): DOI: 10.48550/arxiv.2306.14894
Journal information: Physical Review Letters , arXiv
Provided by Massachusetts Institute of Technology

News
New Blood Test Detects Alzheimer’s and Tracks Its Progression With 92% Accuracy
The new test could help identify which patients are most likely to benefit from new Alzheimer’s drugs. A newly developed blood test for Alzheimer’s disease not only helps confirm the presence of the condition but also [...]
The CDC buried a measles forecast that stressed the need for vaccinations
This story was originally published on ProPublica, a nonprofit newsroom that investigates abuses of power. Sign up to receive our biggest stories as soon as they’re published. ProPublica — Leaders at the Centers for Disease Control and Prevention [...]
Light-Driven Plasmonic Microrobots for Nanoparticle Manipulation
A recent study published in Nature Communications presents a new microrobotic platform designed to improve the precision and versatility of nanoparticle manipulation using light. Led by Jin Qin and colleagues, the research addresses limitations in traditional [...]
Cancer’s “Master Switch” Blocked for Good in Landmark Study
Researchers discovered peptides that permanently block a key cancer protein once thought untreatable, using a new screening method to test their effectiveness inside cells. For the first time, scientists have identified promising drug candidates [...]
AI self-cloning claims: A new frontier or a looming threat?
Chinese scientists claim that some AI models can replicate themselves and protect against shutdown. Has artificial intelligence crossed the so-called red line? Chinese researchers have published two reports on arXiv claiming that some artificial [...]
New Drug Turns Human Blood Into Mosquito-Killing Weapon
Nitisinone, a drug for rare diseases, kills mosquitoes when present in human blood and may become a new tool to fight malaria, offering longer-lasting, environmentally safer effects than ivermectin. Controlling mosquito populations is a [...]
DNA Microscopy Creates 3D Maps of Life From the Inside Out
What if you could take a picture of every gene inside a living organism—not with light, but with DNA itself? Scientists at the University of Chicago have pioneered a revolutionary imaging technique called volumetric DNA microscopy. It builds [...]
Scientists Just Captured the Stunning Process That Shapes Chromosomes
Scientists at EMBL have captured how human chromosomes fold into their signature rod shape during cell division, using a groundbreaking method called LoopTrace. By observing overlapping DNA loops forming in high resolution, they revealed that large [...]
Bird Flu Virus Is Mutating Fast – Scientists Say Our Vaccines May Not Be Enough
H5N1 influenza is evolving rapidly, weakening the effectiveness of existing antibodies and increasing its potential threat to humans. Scientists at UNC Charlotte and MIT used high-performance computational modeling to analyze thousands of viral protein-antibody interactions, revealing [...]
Revolutionary Cancer Vaccine Targets All Solid Tumors
The method triggers immune responses that inhibit melanoma, triple-negative breast cancer, lung carcinoma, and ovarian cancer. Cancer treatment vaccines have been in development since 2010, when the first was approved for prostate cancer, followed [...]
Scientists Uncover Hidden Protein Driving Autoimmune Attacks
Scientists have uncovered a critical piece of the puzzle in autoimmune diseases: a protein that helps release immune response molecules. By studying an ultra-rare condition, researchers identified ArfGAP2 as a key player in immune [...]
Mediterranean neutrino observatory sets new limits on quantum gravity
Quantum gravity is the missing link between general relativity and quantum mechanics, the yet-to-be-discovered key to a unified theory capable of explaining both the infinitely large and the infinitely small. The solution to this [...]
Challenging Previous Beliefs: Japanese Scientists Discover Hidden Protector of Heart
A Japanese research team found that the oxidized form of glutathione (GSSG) may protect heart tissue by modifying a key protein, potentially offering a novel therapeutic approach for ischemic heart failure. A new study [...]
Millions May Have Long COVID – So Why Can’t They Get Diagnosed?
Millions of people in England may be living with Long Covid without even realizing it. A large-scale analysis found that nearly 10% suspect they might have the condition but remain uncertain, often due to [...]
Researchers Reveal What Happens to Your Brain When You Don’t Get Enough Sleep
What if poor sleep was doing more than just making you tired? Researchers have discovered that disrupted sleep in older adults interferes with the brain’s ability to clean out waste, leading to memory problems [...]
How to prevent chronic inflammation from zombie-like cells that accumulate with age
In humans and other multicellular organisms, cells multiply. This defining feature allows embryos to grow into adulthood, and enables the healing of the many bumps, bruises and scrapes along the way. Certain factors can [...]