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SUMMARY:Uncovering Fluctuations in Water through Data-Driven Chemical Phys
 ics - Prof. Ali Hassanali\, ICTP\, Italy
DTSTART:20251110T140000Z
DTEND:20251110T143000Z
UID:TALK239743@talks.cam.ac.uk
CONTACT:Dr Fabian Berger
DESCRIPTION:Aqueous solutions provide the essential medium for countless p
 hysical\, chemical\, and biological processes. Yet\, despite decades of st
 udy\, the microscopic fluctuations that underpin their thermodynamic and d
 ynamical behavior remain elusive. In this talk\, I will present recent eff
 orts from our group to move beyond purely intuition-based approaches towar
 d data-driven frameworks for understanding water’s complexity. Specifica
 lly\, I will introduce an unsupervised learning protocol designed to quant
 ify and interpret high-dimensional fluctuations in liquid water. In chemis
 try\, we often rely on dimensionality reduction to construct simplified co
 nceptual models that shape how we interpret experimental observations. Her
 e\, I will discuss the validity and limitations of such representations in
  two longstanding contexts: the proposed coexistence of high- and low-dens
 ity liquid water\, and the structural motifs of the excess proton (Eigen v
 ersus Zundel). I will conclude with broader reflections on how integrating
  data science with chemical physics can help reconstruct and refine our fo
 undational notions of liquids and their collective behavior.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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