Anisotropic molecular coarse-graining by force and torque matching with neural networks
arXiv:2301.10881 · doi:10.1063/5.0143724
Abstract
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.
arXiv comment13 pages + 8 pages supplementary material, 13 figures
References in corpus (14)
- Adam: A Method for Stochastic Optimization
- On representing chemical environments
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- Machine Learning Force Fields
- Machine learning for molecular simulation
- WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
- An Accurate and Transferable Machine Learning Potential for Carbon
- Deep Potential: a general representation of a many-body potential energy surface
- DeePCG: constructing coarse-grained models via deep neural networks
- On the Continuity of Rotation Representations in Neural Networks
- Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions
- The interplay of interfaces, supramolecular assembly, and electronics in organic semiconductors
- Coarse-graining polymer solutions: a critical appraisal of single- and multi-site models
- Monte Carlo algorithm based on internal bridging moves for the atomistic simulation of thiophene oligomers and polymers