Neural network potentials (NNPs)  have demonstrated the effectiveness of machine-learning tools in the context of atomistic simulations. This approach, which is based on artificial neural networks trained to accurately reproduce ab initio potential energy surfaces, offers two major advantages. First, with respect to the underlying reference method the computational effort to calculate energies and forces is drastically reduced, which allows to sample large system sizes and long time scales in molecular dynamics simulations. In addition, unlike empirical potentials, the neural network at the very heart of the method is not limited by an approximate functional form but can flexibly adjust to the reference potential energy surface. Recently, it has been proposed to extend the machine-learning potential approach to the construction of coarse-grained models . In this pilot project we apply this methodology to neural network potentials and develop a software based on the existing package n2p2 . We intend to construct a coarse-grained model for dendrimer-like DNA molecules  in order to demonstrate the implementation.
 Jochum, C.; Adžić, N.; Stiakakis, E.; Derrien, T. L.; Luo, D.; Kahl, G.; Likos, C. N. Structure and Stimuli-Responsiveness of All-DNA Dendrimers: Theory and Experiment. Nanoscale 2019, 11 (4), 1604–1617.
List of Tasks
- 1. Implement Python tools to generate coarse-grained from fully atomistic data sets
- 2. Devise and implement a procedure to estimate the effectiveness of the coarse-grained description with atomic environment descriptors
- 3. Train and evaluate NNP-CG models for simple (water) and complex (DNA dendrimer) systems
- 4. Improve CG models by inclusion of additional degrees of freedom (e.g. orientation, more particle types)
- 5. Allow large-scale MD simulations with LAMMPS integration
List of Modules
Status: Work in Progress
Expected delivery date: February 2020
Description: The overall goal of the analysis is to show qualitatively whether there is a correlation between the raw atomic environment descriptors (and their
derivatives) and the atomic forces. If no or very little correlation can be found we can assume that the descriptors do not encode enough information to
construct a (free) energy landscape. On the other hand, if "similar" descriptors correspond to "similar" forces there is a good chance that a machine learning
algorithm is capable of detecting this link and a machine learning potential can be fitted. In order to find a possible correlation between descriptors and
forces the following approach is used: First, a clustering algorithm (k-means or HDBSCAN) searches for groups in the high-dimensional descriptor space of all
atoms. Then, for every detected cluster the statistical distribution of the corresponding atomic forces is compared to the statistics of all remaining
atomic forces. A hypothesis test (Welch's t-test) is applied to decide whether the link between descriptors and forces is statistically significant. The
percentage of clusters which show a clear link is then an indicator for a good descriptor-force correlation.
Expected delivery date: -
Description: Integration of additional degrees of freedom such as orientation vectors requires substantial changes in the n2p2 code, in particular for the training procedure.
Expected delivery date: -
Description: A working LAMMPS interface will allow for highly-parallelized simulations with NNP-CG models.