HTESP (High-throughput electronic structure package): a Package for the high-throughput $ab$ $initio$ calculations

Abstract

High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron-phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different $ab$ $initio$ calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, we introduce a software package named "HTESP" (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

References in corpus (15)

• Phonons and related properties of extended systems from density-functional perturbation theory
• VASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
• Wannier90: A Tool for Obtaining Maximally-Localised Wannier Functions
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• WannierTools: An open-source software package for novel topological materials
• High-throughput electronic band structure calculations: challenges and tools
• Pseudopotentials for high-throughput DFT calculations
• AFLOW: An automatic framework for high-throughput materials discovery
• Implementation strategies in phonopy and phono3py
• EPW: Electron-phonon coupling, transport and superconducting properties using maximally localized Wannier functions
• Precision and efficiency in solid-state pseudopotential calculations
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science
• Imaginary phonon modes and phonon-mediated superconductivity in Y2C3
• Origin of Charge Density Wave in Topological Semimetals SrAl4 and EuAl4

Cited by in corpus (3)

• Machine-learning Guided Search for Phonon-mediated Superconductivity in Boron and Carbon Compounds
• Design and Predict Tetragonal van der Waals Layered Quantum Materials of MPd${}_5$I${}_2$ (M=Ga, In and 3$d$ Transition Metals)
• The observation of bulk superconductivity in Rhombohedral ReO3 under pressure