Accelerating the Discovery of Novel Metal Organic Chalcogenolates: A Computational and Machine Learning-Driven Approach

Abstract

Metal Organic Chalcogenolates (MOChas) are a class of robust, self-assembling, and hybrid materials featuring inorganic metalo-chalcogen frameworks that are scaffolded by organic ligands. These low-dimensional structures exhibit tunable optoelectronic properties, making them promising candidates for various applications, including optical sensors and nanotechnology. This tunable relationship between MOCha structural arrangements and targeted properties opens up a vast yet challenging search space for novel MOCha structures. Density Functional Theory (DFT) can predict properties of materials with good accuracy, making it a powerful choice for even hypothetical materials. However, the discovery of novel MOChas structures is constrained by poor scalability of DFT relaxation times for large systems and a lack of high-throughput design methods that can capture the complex geometries of MOChas. In this work, we employ DFT calculations to investigate the energetic and electronic properties of various MOChas, and provide insight into the optical behavior and kinetic favorability of such structures. To address the computational bottlenecks of high-throughput design and DFT workloads, we discuss the use of machine-learned interatomic potentials and various generative models that can enable rapid prototyping of novel MOCha structures.