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InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning
The Interface materials design (InterMat) package 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.
Resources
2 resources available
-
Github of the intermat code on usnistgov
GITH -
NIST software checklist
PDF
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "Kevin Garrity", "hasEmail": "mailto:kevin.garrity@nist.gov" } |
| description | The Interface materials design (InterMat) package 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. |
| distribution |
[ { "title": "Github of the intermat code on usnistgov", "format": "github repo in python, linked to machine learning resources", "accessURL": "https://github.com/usnistgov/intermat", "description": "The Interface materials design (InterMat) package introduces a multi-scale and data-driven approach for material interface/heterostructure design." }, { "title": "NIST software checklist", "format": "pdf", "mediaType": "application/pdf", "description": "NIST software checklist", "downloadURL": "https://data.nist.gov/od/ds/mds2-4023/KFG_final-g-1801.01-appendix-a-ver-1-fillable-form.pdf" } ] |
| identifier | ark:/88434/mds2-4023 |
| issued | 2026-04-08 |
| keyword |
[ "Density functional theory", "defects", "force-field", "interfaces", "intermat", "machine learning", "semiconductors" ] |
| landingPage | https://data.nist.gov/od/id/mds2-4023 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2025-09-10 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| references |
[ "https://pubs.rsc.org/en/content/articlelanding/2024/dd/d4dd00031e" ] |
| theme |
[ "Chemistry:Theoretical chemistry and modeling", "Electronics:Semiconductors", "Materials:Modeling and computational material science", "Nanotechnology:Nanoelectronics", "Physics:Condensed matter" ] |
| title | InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning |
