InterMat-data

Interfaces are critical for a variety of technological applications including semiconductor transistors and diodes, solid-state lighting devices, solar-cells, data-storage and battery applications. While interfaces are ubiquitous, predicting even basic interface properties from bulk data or chemical models remains challenging. Furthermore, the continued scaling of devices towards the atomic limit makes interface properties even more important. There have been numerous scientific efforts to model interfaces with a variety of techniques including density functional theory (DFT), force-field (FF), tight-binding, TCAD and machine learning (ML) techniques. However, to the best of our knowledge, there is no systematic investigation of interfaces for a large class of structural variety and chemical compositions. Most of the previous efforts focus on a limited number of interfaces, and hence there is a need for a dedicated infrastructure for data-driven interface materials design.The Interface materials design (InterMat) package (https://arxiv.org/abs/2401.02021) introduces a multi-scale and data-driven approach for material interface/heterostructure design. This package allows:Generation of an atomistic interface geometry given two similar or different materials,Performing calculations using multi-scale methods such as DFT, MD/FF, ML, TB, QMC, TCAD etc.,analyzing properties such as equilibrium geometries, energetics, work functions, ionization potentials, electron affinities, band offsets, carrier effective masses, mobilities, and thermal conductivities, classification of heterojunctions, benchmarking calculated properties with experiments,training machine learning models especially to accelerate interface design.