Scripts and Models for "Predicting electronic structures at any length scale with machine learning"
Scripts and Models for "Predicting electronic structures at any length scale with machine learning"
Fiedler, L.; Schmerler, S.; Modine, N.; Vogel, D. J.; Popoola, G. A.; Thompson, A.; Rajamanickam, S.; Cangi, A.
Scripts and Models for "Predicting the Electronic Structure of Matter on Ultra-Large Scales" This data set contains scripts and models to reproduce the results of our manuscript "Physics-informed Machine Learning Models for Scalable Density Functional Theory Calculations". The scripts are supposed to be used in conjunction with the ab-initio data sets also published alongside our research article. Requirements python>=3.7.x mala>=1.1.0 ase numpy Contents | Folder name | Description | |------------------|--------------------------------------------------| | data_analysis/ | Run script for RDF calculations | | model_inference/ | Run script to run inference based on MALA models | | model_training/ | Run script to train MALA models | | trained_models/ | Trained models for beryllium and aluminium |
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Reseach data in the HZDR data repository RODARE
Publication date: 2022-09-30 Open access
DOI: 10.14278/rodare.1850
Versions: 10.14278/rodare.1851
License: CC-BY-4.0
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Permalink: https://www.hzdr.de/publications/Publ-35305
Publ.-Id: 35305