Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning

POLYMER

卷:203

Article

Mikami, Koichiro¹ 

[1] Sagami Chem Res Inst, 2743-1 Hayakawa, Ayase, Kanagawa 2521193, Japan

关键词: Machine-learning;    DFT calculation;    Metallocene;    Mechanism;    Descriptor;   

DOI  :  10.1016/j.polymer.2020.122738

来源: Elsevier

【 摘 要 】

Artificial intelligence- and machine learning (ML)-assisted reaction/material development are an emerging research area in organic, organometallic, polymer chemistry and materials science. Quantum chemical descriptors (QCDs) that are classically constructed with steric/electrostatic parameters make the process of the prediction through ML easily understood and allow us to find new chemical pictures for reaction, materials and functionality. Herein, I present the development of novel QCDs-interactive-quantum-chemical-descriptors (IQCDs)-well-expressing an intermolecular interaction among target molecules. The use of IQCDs drastically improved the prediction-accuracy rather than the use of only the classical QCD. One of the IQCDs consists of natural energy decomposition analysis (NEDA), well-expressing a chemical interaction among the molecules/materials, which would be applicable for dynamic processes including formation of chemical bonding, organometallic complex, and supramolecular complex.

【 授权许可】

Free