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DTSTAMP:20260724T151407Z
LOCATION:Bldg. 6 - 001 - Plenary Room
DTSTART;TZID=Europe/Stockholm:20260701T090000
DTEND;TZID=Europe/Stockholm:20260701T110000
UID:submissions.pasc-conference.org_PASC26_sess141@linklings.com
SUMMARY:MS4I - Intelligent Modeling for Sustainable Materials Design: Inte
 grating Physics and Data Across Scales
DESCRIPTION:Access the recording\n\nOrganizer(s): Mattia Turchi, and Ivan 
 Lunati (Empa)\n\nThe transition to a sustainable society critically depend
 s on the discovery of new materials with improved efficiency, durability, 
 and reduced environmental footprint. Achieving this requires transformativ
 e advances in the way materials are conceived, enabling rational design pa
 radigms where atomistic modeling and data-driven methods guide synthesis a
 nd characterization towards target properties. In-silico approaches can st
 reamline this process, but computer-aided materials design remains a compl
 ex multiscale challenge involving phenomena across several spatiotemporal 
 scales. Classical multiscale modeling connects the atomistic resolution at
  the nanoscale with the macroscopic performance of the final product by se
 quentially upscaling the fundamental quantities that control material beha
 vior [1]. More recently, innovative strategies combining data mining of sy
 nthesis and characterization protocols (both in-silico and analytical) wit
 h machine learning regression models have emerged as powerful tools to opt
 imize the synthesis of diverse materials classes [2]. References [1] M. An
 dersson et al. A general, microkinetic model for dissolution of simple sil
 icate and aluminosilicate minerals and glasses as a function of ph and tem
 perature. Chemical Geology, 2025. [2] J. Guo and P. Schwaller. Directly op
 timizing for synthesizability in generative molecular design using retrosy
 nthesis models. Chemical Science, 2025.\n\nData-Efficient Multiscale Learn
 ing for Catalytic Property Prediction on Amorphous Surfaces\n\nAmorphous s
 ilica (a-SiO2) plays a key role in catalysis and gas adsorption. Undercoor
 dinated surface defects enhance reactivity and gas adsorption and can serv
 e as anchoring sites for transition metals. However, the intrinsic structu
 ral disorder of a-SiO2 poses significant challenges for conventiona...\n\n
 \nXuewei Zhang, Mattia Turchi, and Ivan Lunati (Empa)\n-------------------
 --\nChemical Sciences in the Age of LLMs\n\nChemistry faces a fundamental 
 challenge: the space of possible molecules and materials is nearly infinit
 e, yet discovering useful new ones requires navigating costly cycles of de
 sign, synthesis, and testing. In this talk, I will show how language model
 s — the same technology behind modern AI a...\n\n\nPhilippe Schwaller (EPF
 L)\n---------------------\nAn End-to-End Framework for the Evaluation of C
 hemical Reaction Networks\n\nI will revise the developements in my group f
 or the study of catalytic materials. Starting by a reaction network genera
 tor and establishing the rules for the expansion of the chemical space I w
 ill describe the way how we evaluate the energy of the intermediate specie
 s in the reaction pathways. The e...\n\n\nNuria Lopez (ICIQ)\n------------
 ---------\nOpen Discussion: Integrating Multiscale Modeling and Artificial
  Intelligence for Sustainable Materials Discovery\n\nThe rapid convergence
  of physics-based simulations, machine learning, and data-driven methodolo
 gies is reshaping the way catalytic and functional materials are discovere
 d and optimized. This open discussion will explore how multiscale modeling
  and artificial intelligence can jointly accelerate the d...\n\n\nIvan Lun
 ati and Mattia Turchi (Empa)\n\nDomain: Chemistry and Materials, Computati
 onal Methods and Applied Mathematics\n\nSession Chairs: Mattia Turchi (Emp
 a) and Ivan Lunati (Empa)
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