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DTSTART;TZID=Europe/Stockholm:20230626T140000
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UID:submissions.pasc-conference.org_PASC23_sess167@linklings.com
SUMMARY:MS1D - Machine Learning: A Scale-Bridging Tool for Molecular Model
 ing
DESCRIPTION:Minisymposium\n\nMachine learning (ML) methods have dramatical
 ly changed molecular simulations for material and biophysical applications
 . They can provide highly accurate models without increasing the computati
 onal effort, i.e., a bridge between classical quantum, atomistic, and coar
 se-grained spatiotemporal scales. However, several pressing issues still n
 eed to be addressed. Namely, the transferability of ML models (to unseen c
 onfigurations, molecules, and thermodynamic states) and the stability of M
 L-driven simulations (avoiding unphysical states, e.g., overlapping partic
 les). To this end, several novel approaches were recently proposed ranging
  from the sophisticated construction of training databases to the incorpor
 ation of physics knowledge. This minisymposium aims to present state-of-th
 e-art ML methods for molecular modeling and simulations. Additionally, it 
 will provide a platform to discuss the current challenges and share knowle
 dge and ideas across different applications and modeling scales. While the
  primary focus of the minisymposium is on material science and biophysics 
 applications, the novel methodologies tackling data scarcity and predictio
 n uncertainty are transferable to continuum modeling and applications.\n\n
 The End of Ab Initio MD\n\nA new computational task has been defined and s
 olved over the past 15 years for extended material systems: the analytic f
 itting of the Born-Oppenheimer potential energy surface as a function of n
 uclear coordinates under the assumption of medium-range interactions, 5 ~ 
 10 Å. The resulting potentials ...\n\n\nGabor Csanyi (University of Cambri
 dge)\n---------------------\nEnhancing Molecular Dynamics Simulations thro
 ugh Deep Learning\n\nComputational chemistry allows exploration of molecul
 ar systems at atomic resolution. Molecular dynamics (MD) simulations are k
 ey to account for solvent and entropic effects. If an explicit description
  of the system's electronic structure is also required, mixed quantum mech
 anics/molecular mechanics...\n\n\nFelix Pultar, Moritz Thürlemann, and Ser
 eina Riniker (ETH Zurich)\n---------------------\nDeep Coarse-Grained Mole
 cular Modeling\n\nMolecular modeling has become a cornerstone of many disc
 iplines, including material science. However, the quality of predictions c
 ritically depends on the employed model that defines particle interactions
 . A class of models with tremendous success in recent years are neural net
 work (NN) potentials d...\n\n\nJulija Zavadlav (Technical University of Mu
 nich)\n---------------------\nTowards Fully Quantum (Bio)Molecular Modelli
 ng Enabled by HPC and ML: Dream or Reality?\n\nThe convergence between acc
 urate quantum-mechanical (QM) models (along with powerful software and har
 dware) with efficient machine learning (ML) methods seem to promise a para
 digm shift in molecular simulations. Many challenging applications are now
  being tackled by increasingly powerful QM/ML metho...\n\n\nAlexandre Tkat
 chenko (University of Luxembourg)\n\nDomain: Chemistry and Materials\n\nSe
 ssion Chair: Julija Zavadlav (Technical University of Munich)
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