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DTSTAMP:20260605T154542Z
LOCATION:Bldg. 6 - Room 002
DTSTART;TZID=Europe/Stockholm:20260630T134500
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UID:submissions.pasc-conference.org_PASC26_sess141@linklings.com
SUMMARY:MS3A - Intelligent Modeling for Sustainable Materials Design: Inte
 grating Physics and Data Across Scales
DESCRIPTION:Organizer(s): Mattia Turchi, and Ivan Lunati (Empa)\n\nThe tra
 nsition to a sustainable society critically depends on the discovery of ne
 w materials with improved efficiency, durability, and reduced environmenta
 l footprint. Achieving this requires transformative advances in the way ma
 terials are conceived, enabling rational design paradigms where atomistic 
 modeling and data-driven methods guide synthesis and characterization towa
 rds target properties. In-silico approaches can streamline this process, b
 ut computer-aided materials design remains a complex multiscale challenge 
 involving phenomena across several spatiotemporal scales. Classical multis
 cale modeling connects the atomistic resolution at the nanoscale with the 
 macroscopic performance of the final product by sequentially upscaling the
  fundamental quantities that control material behavior [1]. More recently,
  innovative strategies combining data mining of synthesis and characteriza
 tion protocols (both in-silico and analytical) with machine learning regre
 ssion models have emerged as powerful tools to optimize the synthesis of d
 iverse materials classes [2]. References [1] M. Andersson et al. A general
 , microkinetic model for dissolution of simple silicate and aluminosilicat
 e minerals and glasses as a function of ph and temperature. Chemical Geolo
 gy, 2025. [2] J. Guo and P. Schwaller. Directly optimizing for synthesizab
 ility in generative molecular design using retrosynthesis models. Chemical
  Science, 2025.\n\nAn End-to-End Framework for the Evaluation of Chemical 
 Reaction Networks\n\nI will revise the developements in my group for the s
 tudy of catalytic materials. Starting by a reaction network generator and 
 establishing the rules for the expansion of the chemical space I will desc
 ribe the way how we evaluate the energy of the intermediate species in the
  reaction pathways. The e...\n\n\nNuria Lopez (ICIQ)\n--------------------
 -\nData-Efficient Multiscale Learning for Catalytic Property Prediction on
  Amorphous Surfaces\n\nAmorphous silica (a-SiO2) plays a key role in catal
 ysis and gas adsorption. Undercoordinated surface defects enhance reactivi
 ty and gas adsorption and can serve as anchoring sites for transition meta
 ls. However, the intrinsic structural disorder of a-SiO2 poses significant
  challenges for conventiona...\n\n\nXuewei Zhang, Mattia Turchi, and Ivan 
 Lunati (Empa)\n---------------------\nOpen Discussion: Integrating Multisc
 ale Modeling and Artificial Intelligence for Sustainable Materials Discove
 ry\n\nThe rapid convergence of physics-based simulations, machine learning
 , and data-driven methodologies is reshaping the way catalytic and functio
 nal materials are discovered and optimized. This open discussion will expl
 ore how multiscale modeling and artificial intelligence can jointly accele
 rate the d...\n\n\nIvan Lunati and Mattia Turchi (Empa)\n-----------------
 ----\nChemical Sciences in the Age of LLMs\n\nChemistry faces a fundamenta
 l challenge: the space of possible molecules and materials is nearly infin
 ite, yet discovering useful new ones requires navigating costly cycles of 
 design, synthesis, and testing. In this talk, I will show how language mod
 els — the same technology behind modern AI a...\n\n\nPhilippe Schwaller (E
 PFL)\n\nDomain: Chemistry and Materials, Computational Methods and Applied
  Mathematics\n\nSession Chairs: Mattia Turchi (Empa) and Ivan Lunati (Empa
 )
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