A High-Throughput and Data-Driven Computational Framework for Novel Quantum Materials

TL;DR

This paper presents a high-throughput, data-driven computational framework that combines first-principles calculations, material informatics, and machine learning to design and identify stable novel quantum materials through intercalation in layered compounds.

Contribution

It introduces a new computational approach integrating multiple methods to efficiently discover stable intercalated quantum materials from a large configurational space.

Findings

Identified 50 most stable hybrid materials from ~10^5 candidates.

Developed a framework combining first-principles, informatics, and machine learning.

Characterized stability and properties of intercalated layered materials.

Abstract

Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess intrinsic van der Waals gap at the layer interface allowing for remarkable tunability of the optoelectronic features via external intercalation of foreign guests such as atoms, ions, or molecules. Herein, we introduce a high-throughput, data-driven computational framework for the design of novel quantum materials derived from intercalating planar conjugated organic molecules into bilayer transition metal dichalcogenides and dioxides. By combining first-principles methods, material informatics, and machine learning, we characterize the energetic and mechanical stability of this new class of materials and identify the fifty (50) most stable hybrid materials from a vast configurational space comprising $\sim 10^5$ materials, employing intercalation energy as the screening criterion.