High-Throughput $\textit{Ab Initio}$ Design of Atomic Interfaces using InterMatch
Eli Gerber, Steven B. Torrisi, Sara Shabani, Eric Seewald, Jordan, Pack, Jennifer E. Hoffman, Cory R. Dean, Abhay N. Pasupathy, Eun-Ah Kim

TL;DR
InterMatch is a high-throughput computational framework that predicts interfacial properties using bulk material data, enabling efficient design and discovery of novel hetero-interfaces for advanced materials applications.
Contribution
This work introduces InterMatch, a novel open-source algorithm that estimates interfacial properties from bulk data, significantly accelerating interface design efforts.
Findings
Validated charge transfer predictions against experiments and DFT.
Predicted promising interfaces for doping MoSe₂.
Explained superlattice periodicity variations in graphene/RuCl₃.
Abstract
Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface…
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Taxonomy
TopicsMachine Learning in Materials Science · 2D Materials and Applications · Quantum Dots Synthesis And Properties
