Anisotropic molecular coarse-graining by force and torque matching with neural networks

We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network potentials by addressing molecular anisotropy. We demonstrate the flexibility of the method by parametrizing single-site coarse-grained models of a rigid small molecule (benzene) and a semi-flexible organic semiconductor (sexithiophene), attaining structural accuracy close to the all-atom models for both molecules at considerably lower computational expense. The machine-learning method of constructing the coarse-grained potential is shown to be straightforward and sufficiently robust to capture anisotropic interactions and many-body effects. The method is validated through its ability to reproduce the structural properties of the small molecule's liquid phase and the phase transitions of the semi-flexible molecule over a wide temperature range.Comment: 13 pages + 8 pages supplementary material, 13 figure

Machine Learning Anisotropic Coarse-Grained Simulation Models of Small-Molecule and Polymeric Organic Semiconductors

A set of machine learning workflows have been developed to automate the generation of accurate anisotropic coarse-grained models and interaction potentials for small molecules and polymers as well as to analyze the aggregate structure of dilute semiflexible polymers with anisotropic monomers. The multiscale coarse-graining method for isotropic coarse-grained particles has been extended to anisotropic coarse-graining of small molecules and polymers using a mixture of machine learning tools and classical simulation methods. The resulting coarse-grain interaction potentials derived from the machine-learned forcematching approach are flexible and scalable with respect to the type of molecules, the size of the simulation, and the simulation conditions. The robust deep-learning models were specifically used to construct coarse-grained interaction potentials for single-site anisotropic modeling of organic molecules and have shown the capability of reproducing the liquid crystal phase behavior of organic semiconductors. An autoencoder machine learning approach has been used to automate the encoding of atomistic trajectories into unique anisotropic coarse-grained sites. This automated procedure allows for the creation of a simplified representation of organic polymers with the added feature of an accurate back mapping to the atomistic trajectories using the decoder network. Machine learning tools are also developed in this work to analyze and predict the aggregation tendencies of small anisotropic molecules and organic semiconducting polymers in either the liquid or solution phase. A practical deep-learning framework, for the anisotropic coarse-graining of polymers and anisotropic macromolecules, was implemented alongside an automated workflow to predict the polymers’ key aggregation behaviors based on their structure, flexibility, and the simulation condition.Thesis (Ph.D.) -- University of Adelaide, School of Physics, Chemistry and Earth Sciences, 202

Anisotropic force-matching coarse-graining with neural networks data files

Simulation files for all-atom simulation of benzene.</p