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DESCRIPTION:Abstract:\n\nIn this talk Dr. Nicolas Onofrio will present the 
 Amsterdam Modeling Suite (AMS)\, a powerful software package for atomistic
  simulations across diverse levels of theory. AMS offers seamless integrat
 ion with computational engines such as Density Functional Theory\, Tight B
 inding\, and Force Fields\, making it an invaluable platform for exploring
  potential energy surfaces (PES) in molecules and periodic systems. Its in
 novative features include ParAMS\, a module to tune atomistic model parame
 ters as well as an active learning workflow for efficient PES exploration.
  These capabilities enable rapid predictions of material properties and re
 action mechanisms\, driving advancements in batteries\, semiconductors\, p
 olymers\, and OLEDs. With an intuitive Python interface\, AMS empowers res
 earchers to automate workflows\, screen materials\, and optimize critical 
 properties\, providing a comprehensive toolkit for accelerating innovation
  in chemistry and materials science.\n\nMore infos at https://www.scm.com/
 \n\nCo-sponsored by: Dr. Luiz Felipe Aguinsky Postdoctoral Fellow Computat
 ional Nanoelectronics Group Integrated Systems Laboratory\, ETH Zurich ETZ
  H 69\, Gloriastrasse 35 8092 Zurich Switzerland\n\nAgenda: \n\n\nVirtual:
  https://events.vtools.ieee.org/m/471782
LOCATION:Virtual: https://events.vtools.ieee.org/m/471782
ORGANIZER:bettermann@ieee.org
SEQUENCE:30
SUMMARY: Exploring Advanced Materials Modeling with the Amsterdam Modeling 
 Suite (2025 IEEE NTC)
URL;VALUE=URI:https://events.vtools.ieee.org/m/471782
X-ALT-DESC:Description: <br /><p><strong>Abstract:</strong></p>\n<p>In this
  talk Dr. Nicolas Onofrio will present the Amsterdam Modeling Suite (AMS)\
 , a powerful software package for atomistic simulations across diverse lev
 els of theory. AMS offers seamless integration with computational engines 
 such as Density Functional Theory\, Tight Binding\, and Force Fields\, mak
 ing it an invaluable platform for exploring potential energy surfaces (PES
 ) in molecules and periodic systems. Its innovative features include ParAM
 S\, a module to tune atomistic model parameters as well as an active learn
 ing workflow for efficient PES exploration. These capabilities enable rapi
 d predictions of material properties and reaction mechanisms\, driving adv
 ancements in batteries\, semiconductors\, polymers\, and OLEDs. With an in
 tuitive Python interface\, AMS empowers researchers to automate workflows\
 , screen materials\, and optimize critical properties\, providing a compre
 hensive toolkit for accelerating innovation in chemistry and materials sci
 ence.</p>\n<p>&nbsp\;</p>\n<p>More infos at <a href="https://www.scm.com/"
 >https://www.scm.com/</a></p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>
 &nbsp\;</p>\n<p class="MsoNormal">&nbsp\;</p>
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