{"691431":{"#nid":"691431","#data":{"type":"event","title":"Soft Matter Seminar|  Prof. Justin Burton | Emory UN | Host: Dr. Itamar Kolvin","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker: \u003C\/strong\u003EProf. Justin Burton\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EHost: \u003C\/strong\u003EDr. Itamar Kolvin\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle: \u003C\/strong\u003ELearning Force Laws in Many-Body Systems\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EScientific laws describing natural systems may be more complex than our intuition can handle, thus how we discover laws must change. Machine learning (ML) models can analyze large quantities of data, but their structure should match the underlying physical constraints to provide useful insight. While progress has been made using simulated data where the underlying physics is known, training and validating ML models on experimental data requires fundamentally new approaches. Here we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in dusty plasma, a complex, many-body system. In our laboratory dusty plasma, micron-sized, charged particles are levitated in a low-density argon plasma. We image the system using high-speed tomography and track individual particles for minutes. Using the 3D particle trajectories as training data, the ML model accounts for inherent symmetries, non-identical particles, and learns the effective non-reciprocal forces between particles with exquisite accuracy (R^2\u0026gt;0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model\u0027s accuracy enables precise measurements of particle charge and screening length, discovering violations of common theoretical assumptions. Our ability to identify new physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E:\u0026nbsp;Justin\u202fBurton received his B.S. in Physics from the University of Cincinnati in\u202f2001, followed by a Ph.D. in Physics from the University of California,\u202fIrvine in\u202f2006. He then completed postdoctoral research at the Fred\u202fHutchinson Cancer Research Center and the University of Chicago before joining Emory University\u2019s Department of Physics in\u202f2013, where he is currently a Professor of Physics. His honors include selection as a Gordon and Betty Moore Experimental Physics Investigator, election as a Fellow of the American Physical Society, and an NSF\u202fCAREER Award. Burton\u2019s experimental, interdisciplinary lab explores a broad spectrum of complex and nonequilibrium phenomena, with ongoing projects on interfacial and nanoscale fluid dynamics, machine learning approaches to many-body dusty plasma dynamics, and \u201csoft earth\u201d geophysics. Beyond research, he leads several K 12 STEM outreach and education initiatives throughout the Atlanta region.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EScientific laws describing natural systems may be more complex than our intuition can handle, thus how we discover laws must change. Machine learning (ML) models can analyze large quantities of data, but their structure should match the underlying physical constraints to provide useful insight. While progress has been made using simulated data where the underlying physics is known, training and validating ML models on experimental data requires fundamentally new approaches. Here we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in dusty plasma, a complex, many-body system. In our laboratory dusty plasma, micron-sized, charged particles are levitated in a low-density argon plasma. We image the system using high-speed tomography and track individual particles for minutes. Using the 3D particle trajectories as training data, the ML model accounts for inherent symmetries, non-identical particles, and learns the effective non-reciprocal forces between particles with exquisite accuracy (R^2\u0026gt;0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model\u0027s accuracy enables precise measurements of particle charge and screening length, discovering violations of common theoretical assumptions. Our ability to identify new physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E:\u0026nbsp;Justin\u202fBurton received his B.S. in Physics from the University of Cincinnati in\u202f2001, followed by a Ph.D. in Physics from the University of California,\u202fIrvine in\u202f2006. He then completed postdoctoral research at the Fred\u202fHutchinson Cancer Research Center and the University of Chicago before joining Emory University\u2019s Department of Physics in\u202f2013, where he is currently a Professor of Physics. His honors include selection as a Gordon and Betty Moore Experimental Physics Investigator, election as a Fellow of the American Physical Society, and an NSF\u202fCAREER Award. Burton\u2019s experimental, interdisciplinary lab explores a broad spectrum of complex and nonequilibrium phenomena, with ongoing projects on interfacial and nanoscale fluid dynamics, machine learning approaches to many-body dusty plasma dynamics, and \u201csoft earth\u201d geophysics. Beyond research, he leads several K 12 STEM outreach and education initiatives throughout the Atlanta region.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Soft Matter Seminar|  Prof. Justin Burton | Emory UN | Host: Dr. Itamar Kolvin"}],"uid":"30957","created_gmt":"2026-08-04 17:06:19","changed_gmt":"2026-08-04 18:51:37","author":"Shaun Ashley","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-08-25T15:00:00-04:00","event_time_end":"2026-08-25T16:30:00-04:00","event_time_end_last":"2026-08-25T16:30:00-04:00","gmt_time_start":"2026-08-25 19:00:00","gmt_time_end":"2026-08-25 20:30:00","gmt_time_end_last":"2026-08-25 20:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Howey N201\/N202","extras":[],"groups":[{"id":"126011","name":"School of Physics"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}