Machine Learning Reveals New Phase of Solid Hydrogen
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Machine Learning Reveals New Phase of Solid Hydrogen
Hydrogen is the most abundant element in the universe. It has been studied extensively for its behavior under extreme conditions and composition in gaseous planets like Jupiter and Saturn. Scientists have used computer simulations to study how hydrogen atoms interact and combine to form different phases of matter. However, capturing the complete quantum behavior of hydrogen atoms on computers is difficult. Even with state-of-the-art techniques like machine learning-assisted Quantum Monte Carlo (QMC). The researchers have found an unusual solid hydrogen phase that nobody had observed before.
Learn More: What is Machine Learning? A Friendly Introduction for Aspiring Data Scientists and Managers
Uncovering an Unusual Solid Hydrogen Phase
To simplify the task, a team of researchers at the University of Illinois Urbana-Champaign developed a machine learning model trained with Quantum Monte Carlo (QMC) simulations. This method allows quantum mechanical simulations to be performed with an unprecedented number of atoms, making it possible to understand large-scale phase behaviors that require simulating thousands of atoms over a long period.
Through their simulations, the researchers discovered an unusual solid hydrogen phase that had not been previously observed. While the molecules in solid hydrogen are typically close-to-spherical and form a hexagonal close-packed configuration, they kept a stage where the molecules become oblong figures described as egg-like by one of the researchers.
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The researchers surveyed different temperatures and pressures to form a complete picture when they noticed something unusual in the solid phase. While the molecules in solid hydrogen are typically close-to-spherical and include a hexagonal close-packed configuration, they observed a stage where the molecules become oblong figures resembling eggs.
This new behavior showed up at high temperatures and pressures, something older theories did not hint at. Using their machine learning model, the researchers could take full advantage of the most accurate methods and see what was happening.
Implications for Science and Understanding Gaseous Planets
The discovery of this new phase of solid hydrogen through machine learning has significant implications for the field of science. The ability to simulate large-scale phase behaviors using machine learning-assisted Quantum Monte Carlo (QMC) simulations opens up new possibilities for understanding the behavior of other elements and substances in extreme conditions.
Furthermore, discovering this new phase of solid hydrogen enhances our understanding of gaseous planets like Jupiter and Saturn. These planets are primarily made of hydrogen. And understanding the behavior of hydrogen under high temperatures and pressures is crucial to understanding the composition and behavior of these planets.
Learn More: What is Quantum Machine Learning?
Our Say
The discovery has sparked interest from experimentalists who are now revisiting the problem. They are also exploring the behavior of hydrogen under extreme conditions more carefully. New experimental data resulting from this could further refine the theories and simulations surrounding the conduct of hydrogen.
Overall, the discovery of the new phase of solid hydrogen enhances our understanding of gaseous planets like Jupiter and Saturn. Additionally, it also provides insights into the behavior of hydrogen under extreme conditions. The collaboration between theorists and experimentalists will continue to push the boundaries of our understanding its behavior in different situations.
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