Data Sets Utilized in "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys" Article.

The dataset is sourced from the article "Simple Local Environment Descriptors for Accurate Prediction of Hydrogen Absorption and Migration in Metal Alloys." It comprises seven distinct data sets employed for training and testing machine learning models in the article. These datasets are named as follows: <br>1. HEA_TEST.csv <br>2. TS_TRAIN_ALL.csv <br>3. HEA_TRAINING_OCTA_TETRA_TS.csv <br>4. ZPE_HEA_TRAINING_OCTA_TETRA.csv <br>5. TEST_ZPE_SET.csv <br>6. TS_TEST_ALL.csv <br>7. Intermetallics.csv<br>Each dataset contains columns related to hydrogen absorption energy or zero-point energy and various descriptors, including site information, electronegativity average, average number of valence electrons, average ionization energies, mean d-band center, d-band center, mean d-band filling, mean d-band width, maximum phonon center, mean phonon center, sum phonon center, average ionic radius, average atomic radius, average van der Waals radius, average metallic radius, pore radius (in Me units), ideal pore radius (in Me units), interstitial pore type, and alloy composition.<br>This set of descriptors is versatile and can be effectively utilized for predicting hydrogen absorption energy (or zero-point energy) in different interstitial positions within High Entropy Alloys (HEA) and intermetallic alloys. The descriptors capture crucial information about the local environment, facilitating accurate predictions of hydrogen-related properties in various atomic arrangements and configurations found in these materials.