Scientists use GenAI to discover new insights in physics

With the help of

, researchers from MIT and the University of Basel in Switzerland have developed a new machine learning (ML) framework that can help discover new insights about materials science. The results of this study are published in Physical Review Letters. This research uses a neural network-based approach to quickly predict and optimize material properties and characteristics by analyzing large amounts of material data. This GenAI framework is highly automated and efficient and can help accelerate the progress of materials research. The researchers say their framework can be applied to a variety of

#When water transforms from a liquid to a solid, it undergoes important transformation properties, Such as volume and density. Phase changes in water are so common that we don't even think about them seriously, but it's a complex physical system. Predicting the behavior of materials during phase transitions at the molecular level is very complex and challenging.

Researchers at MIT and the University of Basel have harnessed the power of GenAI to create a new framework that can automatically draw phase diagrams of new physical systems and detect interactions between them. Convert. This innovation will bring huge potential to fields such as materials science and chemistry. The framework is based on machine learning algorithms and is able to predict the properties of new materials by learning from known physical models and experimental data. Scientists have long been interested in the understanding of phase transitions at the molecular level. Confused by suddenness and unpredictability. The diversity of materials and their properties, coupled with scarce scientific data, adds to the challenge. That's all set to change with the development of this new framework, which marks a major leap forward in the discovery of new materials and the understanding of their thermodynamic properties. This framework leverages techniques from machine learning and big data analytics to transform our discovery of new materials and significant leaps in our understanding of their thermodynamic properties.

"If you have a new system with completely unknown properties, how do you choose which observable to study? We hope that, at least with data-driven tools, scanning can be done in an automated way Large new systems, and it will point you to important changes in the system. This could be a tool for automated scientific discovery of new, exotic phase properties," says Frank Schäfer, a postdoc in the Julia lab at CSAIL. Co-author of the methods paper.

Julian Arnold, a graduate student at the University of Basel, was responsible for the first project related to the research; also included Alan Edelman, head of the laboratory of Julia, Professor of Applied Mathematics at the Department of Mathematics; and Physics at the University of Basel Department professor and senior author Christoph Bruder.

This research breakthrough makes it possible for scientists to discover unknown phases of matter. The transition of water from liquid to solid is the most obvious example of a phase change. There are other more complex and complicated material transitions, such as when a material's conductivity changes from state to state.

Traditional scientific methods rely on theoretical explanations of physical states while requiring scientists to manually construct phase diagrams. These methods have serious limitations, including the inability to produce phase diagrams for highly complex systems, the risk of human bias, and being limited to theoretical assumptions about which parameters are important. However, as computer technology advances, new scientific methods are being developed. One of them is a machine learning-based approach that leverages computing power and big data analytics to infer the phase diagram of a physical system. This method no longer relies on artificial assumptions and is capable of handling complex systems because it can handle large amounts of experimental data and variables. The development of these new methods is important to the scientific community. A research team from MIT and the University of Basel used a physics-informed GenAI model to analyze "order parameters", This is a measurable quantity that indicates the ratio of total phase modulators to disordered phase modulators. For example, an order parameter can be used to define the ratio of water molecules in an ordered state to those in a disordered state.

The Julia programming language, known for its excellence in scientific and technical computing, plays an important role in building new ML models. The method published in the paper reportedly outperforms other ML techniques in terms of computational efficiency.

This research has the potential to transform the fields of materials science and quantum physics. Not only can the new framework be used to solve classification tasks in physical systems, but it can also play a key role in improving large language models (LLMs) by determining how to fine-tune certain parameters to get better output.

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