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.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
NTT Service Evolution Laboratories, NTT Corporation（日本电信电话株式会社NTT服务进化实验室）；Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；NTT Communication Science Laboratories, NTT Corporation（日本电信电话株式会社NTT通信科学实验室）
NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）；Suzhou Laboratory（苏州实验室）；Key Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University（吉林大学物理学院材料模拟方法与软件教育部重点实验室）
Understanding Dynamics in Coarse-Grained Models: IV. Connection of Fine-Grained and Coarse-Grained Dynamics with the Stokes-Einstein and Stokes-Einstein-Debye Relations
