Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?

ABSTRACT: There is an exigency of adopting machine learning techniques to screen and discover new materials which could address many societal and technological challenges. In this work, we follow this trend and employ machine learning to study (high-order) electric properties of organic compounds. The results of quantum-chemistry calculations of polarizability and first hyperpolarizability, obtained for more than 50,000 compounds, served as targets for machine learning-based predictions. The studied set of molecular structures encompasses organic push-pull molecules with variable linker lengths. Moreover, the diversified set of linkers, composed of alternating single/double and single/triple carbon-carbon bonds, was considered. This study demonstrates that the applied machine learning strategy allow

SUBMITTER: Tuan-Anh T 

PROVIDER: S-EPMC7081434 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature