BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230831T095754Z
LOCATION:Schwarzhorn
DTSTART;TZID=Europe/Stockholm:20230626T163000
DTEND;TZID=Europe/Stockholm:20230626T183000
UID:submissions.pasc-conference.org_PASC23_sess117@linklings.com
SUMMARY:MS2D - Computer-Aided Design of High-Performance Thermoelectric Ma
 terials
DESCRIPTION:Minisymposium\n\nThe heat-to-electricity conversion (one aspec
 t of thermoelectricity) is known for more than two centuries. However, the
 rmoelectricity has hardly found its way to large-scale deployment due to t
 he lack of materials with a figure of merit ZT greater than 2. One reason 
 for this is that the figure of merit combines properties that counteract o
 ne another. Another reason is that the experimental search for new TE mate
 rials have reached a plateau in terms of chemical diversity, design, and m
 anufacturing of materials, leading to best material ZT values of at most 1
 .6-1.9. A new paradigm has to be found to relaunch the discovery of TE mat
 erials, and the computer-aided design of materials could be such a paradig
 m. In this mini-symposium we will discuss the recently developed algorithm
 s (e.g., basin hoping, evolutionary, genetic algorithms) that foster the d
 iscovery of new chemical structures, the artificial-intelligence-based one
 s to predict both new structures and their properties and their potential 
 synergistic association to unveil new generation of thermoelectrics and im
 prove their efficiency.\n\nHigh-Throughput Search for Optimal Thermoelectr
 ic Performance across the Materials Cloud Wannier Database\n\nWe perform a
  first-principles, high-throughput search for bulk materials displaying op
 timal thermoelectric performance. For this, we screen the Materials Cloud 
 database of experimentally known 3D crystals (MC3D, https://www.materialsc
 loud.org/discover/mc3d), and capture their electronic structure us...\n\n\
 nNicola Marzari (EPFL, Paul Scherrer Institute); Giovanni Pizzi (Paul Sche
 rrer Institute); and Junfeng Qiao (EPFL)\n---------------------\nLattice A
 nharmonicity in Solids: High-Throughput Screening and Machine Learning\n\n
 Despite important breakthroughs in the last decade, the calculation of tem
 perature dependent properties of solids still remains a challenging task, 
 especially in the vicinity of structural phase transitions [1]. In this ta
 lk, I will show examples of computational high-throughput screening of the
 rmal...\n\n\nAmbroise van Roekeghem (Université Grenoble Alpes)\n---------
 ------------\nA Machine-Learning Based Hierarchical Framework to Discover 
 Novel Functional Materials\n\nThe compositional and structural variety inh
 erent to oxide perovskites and their fascinating properties spawn wide-ran
 ging applications from electromechanical devices to opto-electronic materi
 als for radiation detection. The band gap in these materials can be optima
 lly controlled by varying the comp...\n\n\nAnjana Talapatra (Los Alamos Na
 tional Laboratory)\n---------------------\nPanel Discussion\n\nPanel discu
 ssion\n\n\nPascal Boulet (Aix-Marseille University)\n\nDomain: Chemistry a
 nd Materials\n\nSession Chair: Pascal Boulet (Aix-Marseille University)
END:VEVENT
END:VCALENDAR