{"id":506082,"date":"2021-12-15T12:20:31","date_gmt":"2021-12-15T17:20:31","guid":{"rendered":"https:\/\/www.rochester.edu\/newscenter\/?p=506082"},"modified":"2023-09-28T12:22:47","modified_gmt":"2023-09-28T16:22:47","slug":"rochester-scientists-reveal-the-limits-of-machine-learning-for-hydrogen-models-506082","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/rochester-scientists-reveal-the-limits-of-machine-learning-for-hydrogen-models-506082\/","title":{"rendered":"Rochester scientists reveal the limits of machine learning for hydrogen models"},"content":{"rendered":"

Research from the Laboratory for Laser Energetics paves the way for more accurate computer models, which are needed to understand the interior of planets and the physical properties of nuclear fusion.<\/h2>\n

Hydrogen is one of the most abundant elements in the universe.<\/p>\n

On Earth, hydrogen is normally a gas. But when it is under high temperatures and pressures\u2014the conditions that exist within many planets, such as Jupiter\u2014hydrogen goes through a series of phase transitions and takes on the properties of a liquid metal. One of the metallic properties it takes on is becoming an electrical conductor.<\/p>\n

In a new paper<\/a> in Nature<\/em>\u2019s \u201cMatters Arising,\u201d researchers at the Ä¢¹½´«Ã½<\/a> Laboratory for Laser Energetics<\/a> (LLE), including lead author Valentin Karasiev<\/a>, an LLE staff scientist; graduate student Josh Hinz; and Suxing Hu<\/a>, an associate professor of mechanical engineering<\/a> and a distinguished scientist at the LLE, respond to a 2020 Nature <\/em>paper<\/a> that used machine learning techniques to study the liquid-liquid phase transitions of dense hydrogen from an insulating liquid to a liquid metal.<\/p>\n

In their response, Karasiev and his colleagues outline how these machine learning techniques produced incorrect results in describing hydrogen\u2019s phase transitions. Their research has important implications in building more accurate computer models to study hydrogen, which can lead to a better understanding of the interiors of planets and stars and the physical properties of processes like nuclear fusion.<\/p>\n

When building the equation-of-state of hydrogen\u2014the equation that describes the state of hydrogen under various physical conditions\u2014it is important to characterize the transition into the metallic hydrogen phase: Is it an abrupt (sharp) transition or a smooth transition?<\/p>\n

\u201cThis physics character of first-order phase transition can have profound implications in understanding what giant planets\u2019 interior structures look like, such as de-mixing of hydrogen and helium in Jupiter,\u201d Hu says.<\/p>\n

In the 2020 Nature<\/em> paper, researchers used machine learning and concluded the transition of hydrogen to the metallic hydrogen phase was smooth. Karasiev and his colleagues, however, performed large-scale quantum simulations using other fundamental density-functional theory and found that hydrogen\u2019s transition is not smooth, but is instead more abrupt. This is consistent with other previous data collected without machine learning.<\/p>\n

\u201cOur work demonstrated that machine learning can fool scientists if they are not careful when using machine learning to study phase-transition boundaries,\u201d Karasiev says. \u201cThis is an important step in building better models to outline how hydrogen can become metallic hydrogen.\u201d<\/p>\n

The National Science Foundation and the US Department of Energy National Nuclear Security Administration funded this research.<\/p>\n

\n

Read more<\/strong><\/h3>\n
\n
\""\"Researchers unravel more mysteries of metallic hydrogen<\/strong><\/a><\/p>\n

<\/strong><\/a>Scientists at the University\u2019s Laboratory for Laser Energetics. LLE scientists are researching the properties of liquid metallic hydrogen to understand how planets both inside and outside our solar system form magnetic shields.<\/span><\/div>\n

\"close-up A \u2018new chapter\u2019 in quest for novel quantum materials<\/p>\n

<\/strong><\/a>Aluminum stops being a metal. Metals turns transparent. Remarkable things happen to materials placed under remarkable conditions, and Rochester scientists are at the forefront of the quest to understand why.<\/span><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Research from the Laboratory for Laser Energetics paves the way for more accurate computer models, which are needed to understand the interior of planets and the physical properties of nuclear fusion.<\/p>\n","protected":false},"author":912,"featured_media":506122,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[23312,18632,5296,18572,13272],"class_list":["post-506082","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-department-of-mechanical-engineering","tag-hajim-school-of-engineering-and-applied-sciences","tag-laboratory-for-laser-energetics","tag-research-finding","tag-suxing-hu"],"acf":[],"yoast_head":"\nRochester scientists reveal the limits of machine learning for hydrogen models<\/title>\n<meta name=\"description\" content=\"New research provides more accurate data on hydrogen's phase transition to 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