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DTSTAMP:20260421T090513Z
LOCATION:Bldg. 6 - Room 002
DTSTART;TZID=Europe/Stockholm:20260630T134500
DTEND;TZID=Europe/Stockholm:20260630T154500
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:The transition to a sustainable society critically depends on 
 the discovery of new materials with improved efficiency, durability, and r
 educed environmental footprint. Achieving this requires transformative adv
 ances in the way materials are conceived, enabling rational design paradig
 ms where atomistic modeling and data-driven methods guide synthesis and ch
 aracterization towards target properties. In-silico approaches can streaml
 ine this process, but computer-aided materials design remains a complex mu
 ltiscale challenge involving phenomena across several spatiotemporal scale
 s. Classical multiscale modeling connects the atomistic resolution at the 
 nanoscale with the macroscopic performance of the final product by sequent
 ially upscaling the fundamental quantities that control material behavior 
 [1]. More recently, innovative strategies combining data mining of synthes
 is and characterization protocols (both in-silico and analytical) with mac
 hine learning regression models have emerged as powerful tools to optimize
  the synthesis of diverse materials classes [2]. References [1] M. Anderss
 on et al. A general, microkinetic model for dissolution of simple silicate
  and aluminosilicate minerals and glasses as a function of ph and temperat
 ure. Chemical Geology, 2025. [2] J. Guo and P. Schwaller. Directly optimiz
 ing for synthesizability in generative molecular design using retrosynthes
 is models. Chemical Science, 2025.\n\nChemical Sciences in the Age of LLMs
 \n\nChemistry faces a fundamental challenge: the space of possible molecul
 es and materials is nearly infinite, yet discovering useful new ones requi
 res navigating costly cycles of design, synthesis, and testing. In this ta
 lk, I will show how language models — the same technology behind modern AI
  a...\n\n\nPhilippe Schwaller (EPFL)\n---------------------\nA Multiscale,
  Multiphysics Framework for Multiphase Fluid Flow in a Complex Subsurface 
 Environment: Application to Sustainable Enhanced Oil Recovery\n\nWe have d
 eveloped a computational framework for seamlessly bridging molecular scale
  properties via the pore and core scale all the way to the field scale for
  multiphase flow in porous media. \n\nWe combined thermodynamic and kineti
 c effects on the molecular scale using density functional theory, COSMO...
 \n\n\nMartin Andersson, Fernando Alonso Marroquin, Mohamed Gamal Rezk, and
  Safwat Abdel-Azeim (King Fahd University of Petroleum and Minerals)\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\nDomain: Chemistry and Materials, Computational Methods an
 d Applied Mathematics\n\nSession Chairs: Mattia Turchi (Empa) and Ivan Lun
 ati (Empa)
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