From: chemistry-request at ccl.net <br />
To: chemistry-request at ccl.net<br />
Date: Wed Apr  2 03:22:01 2025<br />
Subject: 25.12.15 <span class="fancysmall">CTH005: Computational Photocatalysis:  Photo-Physics &amp; Photo-Chemistry  at Interfaces. Machine Learning Bridges Theory and Experiment</span><br />

<pre>Submitting an abstract at the Honolulu PacifiChem website, 
the code for the symposium is CTH005, 
it is listed together with &quot;Computational and Theoretical Session&quot;. 
The deadline for abstract submission is April 2nd
Please submit your abstract at
<a href="https://pacifichem2025.abstractcentral.com/submission"
target="_blank">https://pacifichem2025.abstractcentral.com/submission</a>.<br />
The Poster for the Symposium can be taken from
<a href="Poster_PACIFICHEM_PPT_V39.pdf" target="_blank">Poster_PACIFICHEM_PPT_V39.pdf</a><br />

The study of photochemical reactions in general 
and photoelectrochemical water splitting in particular, 
rests on understanding of such elementary effects as 
light absorption, energy transfer, electron transfer, 
radiative and nonradiative relaxation, and catalysis 
is important for the rational design of efficient systems 
for energy conversion. 
The design of most efficient catalysts is pursued by change of composition, 
quantum confinement, size, shape, surface functionalization, magnetic doping,
 and mesoscale structural arrangement providing versatile tuning of 
timescales of available basic mechanisms and properties of materials. 
This symposium presents recent experimental, computational, 
and machine learning synergistic advances on modeling of 
photophysics and photochemistry at interfaces: 
Experimental achievements in fabrication of efficient 
photocatalytic interfaces and monitoring of efficiency, quantum yield, 
and kinetics of reactant evolution and electronic dynamics by ultrafast 
spectroscopy techniques stimulate further development of 
more precise theoretical methods. 
Computational modeling allows for interpretation of available experimental 
trends and help in guiding further advances in design of 
efficient photocatalytic materials.
Cheminformatics and machine learning advances help to establish
 a feedback loop between computation and experiment and narrow down 
the number of structures with high potential for record efficiency. 
It is expected that the symposium will bring better understanding of 
photoinduced processes of light absorption, 
formation and breaking of charge transfer excitations, 
hot carrier relaxation, multiple exciton processes, 
coupled light-to-matter states, 
and redox reaction dynamics at catalytic sites, 
affected by lattice vibrations and solvent polarization.

Chemical transformations at a contact with catalyst activated by 
photoactivation open new opportunities for experiment and 
practical applications and offer inspiring challenges to computations, 
theory, and machine learning. This symposium aims to present current 
frontiers in theoretical, computational, machine learning, 
and experimental studies of photoreactions and photophysical properties 
of nanostructures and interfaces in order to facilitate a synergistic 
interdisciplinary effort towards design and characterization of 
novel materials for energy applications and lighting technology. 
The central idea of the symposium is to bring together experts 
from computational, machine learning, and experimental communities 
addressing main challenges in photoinduced charge/energy transfer 
and photocatalytic reactions at interfaces and nanostructures.</pre>
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