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Published September 9, 2022 | Version v1
Conference paper Open

Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules

  • 1. Institute of Nanoscience and Nanotechnology National Center for Scientific Research "Demokritos", Athens, Greece & School of Chemical Engineering National Technical University of Athens, Athens, Greece
  • 2. Institute of Informatics and Telecommunications & Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research "Demokritos", Athens, Greece
  • 3. Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Athens, Greece & Scify P.N.P.C., Athens, Greece
  • 4. Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Athens, Greece
  • 5. School of Chemical Engineering, National Technical University of Athens, Athens, Greece
  • 6. Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research "Demokritos", Athens, Greece

Description

Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study. Coarse-graining (CG) allows to establish a link between different system resolutions and provides the backbone for the development of robust multiscale simulations and analyses. The CG mapping process is typically system- and application-specific, and it relies on chemical intuition. In this work, we explored the application of a Machine Learning strategy, based on Variational Autoencoders, for the development of suitable mapping schemes from the atomistic to the coarse-grained space of molecules with increasing chemical complexity. An extensive evaluation of the effect of the model hyperparameters on the training process and on the final output was performed, and an existing method was extended with the definition of different loss functions and the implementation of a selection criterion that ensures physical consistency of the output. The relationship between the input feature choice and the reconstruction accuracy was analyzed, supporting the need to introduce rotational invariance into the system. Strengths and limitations of the approach, both in the mapping and in the backmapping steps, are highlighted and critically discussed.

Notes

This work was supported by computational time granted from the National Infrastructures for Research and Technology S.A. (GRNET S.A.) in the National HPC facility - ARIS - under the projects COMPIL2 and MULTIPOLS (ID: 009014, 011032).

Files

Investigation_of_Machine_Learning_based_Coarse_Grained_Mapping_Schemes_for_Organic_Molecules_SETN2022.pdf

Additional details

Funding

ML-MULTIMEM – Machine Learning-aided Multiscale Modelling Framework for Polymer Membranes 101030668
European Commission