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DTSTAMP:20250822T115807Z
LOCATION:Room 5.2A17
DTSTART;TZID=Europe/Stockholm:20250616T150000
DTEND;TZID=Europe/Stockholm:20250616T153000
UID:submissions.pasc-conference.org_PASC25_sess145_msa220@linklings.com
SUMMARY:AI and Molecular Modeling for Sustainable Phosphorus Management
DESCRIPTION:Yaroslava Yingling (North Carolina State University)\n\nPhosph
 orus is an essential element for life and a critical component of agricult
 ural fertilizers, yet its sustainable management remains a pressing global
  challenge. Excess phosphorus runoff contributes to environmental pollutio
 n, while limited high-quality phosphate rock reserves raise concerns about
  future availability. This study explores the intersection of AI and molec
 ular modeling to advance phosphorus sustainability. By leveraging molecula
 r dynamics simulations and AI-driven predictive modeling, we investigate t
 he interactions between phosphorus-binding proteins and various phosphorus
  species to enhance selective capture and recycling strategies. Machine le
 arning algorithms are applied to predict modular peptide sequences with hi
 gh binding affinity for phosphorus, facilitating the design of novel biomi
 metic materials for phosphorus recovery. Additionally, we utilize AI-power
 ed data integration to analyze large-scale phosphorus-related datasets, en
 abling more efficient resource utilization and policy development. This in
 terdisciplinary approach has the potential to revolutionize phosphorus man
 agement by improving recovery efficiency, reducing environmental impact, a
 nd ensuring long-term sustainability.\n\nDomain: Chemistry and Materials, 
 Climate, Weather, and Earth Sciences, Applied Social Sciences and Humaniti
 es, Engineering, Life Sciences, Computational Methods and Applied Mathemat
 ics\n\nSession Chair: Miroslava Nedyalkova (University of Fribourg)\n\n
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