Multi-fidelity prediction of molecular optical peaks with deep learning.

ABSTRACT: Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks

SUBMITTER: Greenman KP 

PROVIDER: S-EPMC8790778 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature