{"id":328013,"date":"2025-08-08T13:50:10","date_gmt":"2025-08-08T13:50:10","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/328013\/"},"modified":"2025-08-08T13:50:10","modified_gmt":"2025-08-08T13:50:10","slug":"this-homemade-desktop-ai-has-discovered-new-laws-of-physics-overturning-decades-of-plasma-science","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/328013\/","title":{"rendered":"This Homemade \u201cDesktop AI\u201d Has Discovered New Laws of Physics, Overturning Decades of Plasma Science"},"content":{"rendered":"

In a peer-reviewed study published in Proceedings of the National Academy of Sciences (PNAS)<\/a>, physicists at Emory University have built a custom artificial intelligence system<\/strong> that did more than analyze data\u2014it revealed entirely new physical behaviors <\/strong>in a complex state of matter known as dusty plasma<\/strong>.<\/p>\n

The researchers\u2014led by Ilya Nemenman and Justin Burton\u2014designed the AI to learn from a small set of 3D experimental data<\/strong>. What it found was unexpected: non-reciprocal interactions<\/strong> between particles and structural corrections to decades-old scientific assumptions.<\/p>\n

What Is Dusty Plasma\u2014And Why Is It So Weird?<\/p>\n

Dusty plasma<\/strong>, or complex plasma<\/strong>, is a high-temperature ionized gas<\/strong> that contains tiny dust particles<\/a>, and it shows up in places ranging from Saturn\u2019s rings<\/a> and lunar dust clouds to wildfire smoke on Earth. While this material is common in space and astrophysical environments, it behaves in unusual ways that have long puzzled researchers.<\/p>\n

In typical systems, forces between particles are reciprocal\u2014if one particle pushes or pulls on another, the reverse is also true. But in dusty plasma, that rule breaks down<\/strong>. Forces become asymmetric: a leading particle can pull the one behind it, while the trailing particle repels the leader. This concept, known as non-reciprocal interaction<\/strong>, had been theorized but was never experimentally confirmed\u2014until now.<\/p>\n

The research team developed a custom 3D imaging setup<\/strong> using a high-speed camera<\/a> and laser sheets to track the motion of plastic dust particles in a plasma-filled chamber. From these motion trails, they trained a purpose-built neural network, embedding known physical rules like gravity, drag, and interaction forces directly into the model.<\/p>\n

AI Built for Discovery\u2014Not Just Prediction<\/p>\n

What sets this work apart is how the AI was used. Most machine learning tools in science are designed to predict outcomes<\/strong><\/a> or clean up noisy datasets. This system was trained to discover new rules<\/strong>.<\/p>\n

\u201cWe showed that we can use AI to discover new physics,\u201d said Justin Burton, a physicist and co-author of the study. \u201cOur AI method is not a black box: we understand how and why it works.\u201d Instead of working with millions of datapoints, the model relied on a smaller but highly detailed dataset<\/strong>, structured to allow for physical interpretation.<\/p>\n

The neural network broke down particle motion into three components: drag-based velocity, environmental forces like gravity, and particle-to-particle interactions. Using this structure, it identified and described non-reciprocal forces<\/strong> with over 99% accuracy<\/strong>\u2014a level of precision typically out of reach for traditional experimental methods.<\/p>\n

Correcting Long-Standing Assumptions in Plasma Physics<\/p>\n

Beyond mapping new interactions, the AI helped refine or overturn<\/strong> some long-accepted ideas in plasma physics. One assumption was that a particle\u2019s electric charge increased directly with its size<\/strong>. The AI found that this wasn\u2019t always true\u2014the relationship also depends on the plasma\u2019s density and temperature<\/strong>.<\/p>\n

Another textbook belief was that the inter-particle force weakens exponentially with distance<\/strong>, regardless of size. In fact, the model showed that the rate of decay changes depending on particle size<\/strong>, suggesting more nuanced dynamics at play.<\/p>\n

\u201cSome common theoretical assumptions about these forces are not quite accurate,\u201d said Nemenman. \u201cWe\u2019re able to correct these inaccuracies because we can now see what\u2019s occurring in such exquisite detail.\u201d<\/p>\n

Implications Far Beyond Plasma Research<\/p>\n

Perhaps just as noteworthy as the findings themselves is the tool used to uncover them<\/strong>. This AI model didn\u2019t require a supercomputer or cloud infrastructure\u2014it ran on a standard desktop computer<\/a><\/strong>. Its accessibility makes the approach appealing for a wide range of scientific fields beyond plasma physics.<\/p>\n

The researchers believe their method can be adapted to study many-body systems<\/strong> in biology (like cell migration<\/strong>) or industry (such as paint and material mixtures<\/strong>), where complex interactions are difficult to map using classical equations. Because the AI was designed with explainability<\/strong> in mind, it avoids the \u201cblack box\u201d issue that plagues many machine learning approaches.<\/p>\n

\u201cFor all the talk about how AI is revolutionizing science,\u201d Nemenman said, \u201cthere are very few examples where something fundamentally new has been found directly by an AI system.\u201d This work, he hopes, will be the start of more.<\/p>\n","protected":false},"excerpt":{"rendered":"In a peer-reviewed study published in Proceedings of the National Academy of Sciences (PNAS), physicists at Emory University…\n","protected":false},"author":2,"featured_media":328014,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3845],"tags":[74,70,16,15],"class_list":{"0":"post-328013","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-physics","8":"tag-physics","9":"tag-science","10":"tag-uk","11":"tag-united-kingdom"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/114993472104269268","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/328013","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=328013"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/328013\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/328014"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=328013"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=328013"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=328013"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}