Atomic-Scale Mechanistic Insights into the Ring-Opening Polymerization of Elemental Sulfur.

A detailed understanding of the composition and polymerization mechanism of elemental sulfur remains a decades long unresolved question for modern chemistry. However, the dynamic nature of molten sulfur significantly complicates its accurate characterization. To overcome this challenge, we performed the first comprehensive molecular dynamics (MD) simulations using a ReaxFF reactive force field specifically parameterized to capture the complex ring-opening polymerization dynamics of elemental sulfur.

Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach.

Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead.

Machine Learning Models for Predicting Zirconocene Properties and Barriers.

Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images.

Efficient Embedded Cluster Density Approximation Calculations with an Orbital-Free Treatment of Environments.

The computational cost of the Kohn-Sham density functional theory (KS-DFT), employing advanced orbital-based exchange-correlation (XC) functionals, increases quickly for large systems. To tackle this problem, we recently developed a local correlation method in the framework of KS-DFT: the embedded cluster density approximation (ECDA). The aim of ECDA is to obtain accurate electronic structures in an entire system.

Precise Design of Phosphorescent Molecular Butterflies with Tunable Photoinduced Structural Change and Dual Emission.

Photoinduced structural change (PSC) is a fundamental excited-state dynamic process in chemical and biological systems. However, precise control of PSC processes is very challenging, owing to the lack of guidelines for designing excited-state potential energy surfaces (PESs). A series of rationally designed butterfly-like phosphorescent binuclear platinum complexes that undergo controlled PSC by Pt-Pt distance shortening and exhibit tunable dual (greenish-blue and red) emission are herein reported.