{"id":290,"date":"2024-05-16T07:55:44","date_gmt":"2024-05-16T07:55:44","guid":{"rendered":"http:\/\/localhost:8888\/sawberries\/2024\/05\/16\/scientists-use-generative-ai-complex-questions-physics-0516\/"},"modified":"2024-05-16T07:55:44","modified_gmt":"2024-05-16T07:55:44","slug":"scientists-use-generative-ai-complex-questions-physics-0516","status":"publish","type":"post","link":"http:\/\/localhost:8888\/sawberries\/2024\/05\/16\/scientists-use-generative-ai-complex-questions-physics-0516\/","title":{"rendered":"Scientists use generative AI to answer complex questions in physics"},"content":{"rendered":"
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\u2019t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.<\/p>\n
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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
\u201cIf 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,\u201d says Frank Sch\u00e4fer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.<\/p>\n
Joining Sch\u00e4fer 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<\/a> in Physical Review Letters.<\/em><\/p>\n Detecting phase transitions using AI<\/strong><\/p>\n 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.<\/p>\n These transitions can be detected by identifying an \u201corder parameter,\u201d 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.<\/p>\n