Electronic coarse-graining via supervised machine learning

April 17, 2019

In a study published in Science Advances, researchers presented a machine learning approach in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations.

Schematic of the ANN-ECG method used in this work. Schematic example shows a three-bead/monomer CG mapping for S3MT.

Scientific Achievement

Artificial Neural Network-Electronic Coarse Graining (ANN-ECG), a hybrid machine learning protocol, enables efficient and accurate mapping of atomistic electronic structure onto coarse-grained representations of soft matter simulations.

Significance and Impact

ANN-ECG accelerates optoelectronic characterization in soft semiconductors by ~4 orders of magnitude, and provides physical insight for defining coarse-grained representations.

Research Details

The conformation-dependent electronic structure of polythiophene is predicted directly at the coarse-grained resolution with unprecedented accuracy using machine learning. Such calculations are not feasible at the purely atomistic level.
Accurate machine learning models can be trained with minimal data (1,000’s of configurations).
ANN-ECG can be straightforwardly extended to treat condensed phases and polymeric materials.

Work was performed at Argonne National Laboratory.

DOI: https://doi.org/10.1126/sciadv.aav1190

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