Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2026-09-19
Metadata Last Updated:
2025-09-10 00:00:00
Date Created:
N/A
Data Provided by:
Dataset Owner:
N/A
| Title | InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning |
|---|---|
| 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. |
| Modified | 2025-09-10 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | Density functional theory , force-field , machine learning , semiconductors , interfaces , defects , intermat |
{
"identifier": "ark:\/88434\/mds2-4023",
"accessLevel": "public",
"contactPoint": {
"hasEmail": "mailto:[email protected]",
"fn": "Kevin Garrity"
},
"programCode": [
"006:045"
],
"landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-4023",
"title": "InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning",
"description": "The Interface materials design (InterMat) package introduces a multi-scale and data-driven approach for material interface\/heterostructure design. This package allows:\n\n-Generation of an atomistic interface geometry given two similar or different materials,\n-Performing calculations using multi-scale methods such as DFT, MD\/FF, ML, TB, QMC, TCAD etc.,\n-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,\n-training machine learning models especially to accelerate interface design.",
"language": [
"en"
],
"distribution": [
{
"accessURL": "https:\/\/github.com\/usnistgov\/intermat",
"format": "github repo in python, linked to machine learning resources",
"description": "The Interface materials design (InterMat) package introduces a multi-scale and data-driven approach for material interface\/heterostructure design.",
"title": "Github of the intermat code on usnistgov"
},
{
"format": "pdf",
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-4023\/KFG_final-g-1801.01-appendix-a-ver-1-fillable-form.pdf",
"description": "NIST software checklist",
"mediaType": "application\/pdf",
"title": "NIST software checklist"
}
],
"bureauCode": [
"006:55"
],
"modified": "2025-09-10 00:00:00",
"publisher": {
"@type": "org:Organization",
"name": "National Institute of Standards and Technology"
},
"theme": [
"Chemistry:Theoretical chemistry and modeling",
"Electronics:Semiconductors",
"Materials:Modeling and computational material science",
"Physics:Condensed matter",
"Nanotechnology:Nanoelectronics"
],
"keyword": [
"Density functional theory",
"force-field",
"machine learning",
"semiconductors",
"interfaces",
"defects",
"intermat"
]
}
