| Autor: |
Fiedler, L., Schmerler, S., Modine, N., Vogel, D. J., Popoola, G. A., Thompson, A., Rajamanickam, S., Cangi, A. |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Zdroj: |
Publication date: 2022-09-30 Open accessDOI: 10.14278/rodare.1850Versions: 10.14278/rodare.1851License: CC-BY-4.0 |
| Popis: |
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 |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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