Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations

Abstract

In this work, we developed an automatic convergence procedure for k-points and plane wave cut-off in density functional (DFT) calculations and applied it to more than 30000 materials. The computational framework for automatic convergence can take a user-defined input as a convergence criterion. For k-points, we converged energy per cell (EPC) to 0.001 eV/cell tolerance and compared the results with those obtained using an energy per atom (EPA) convergence criteria of 0.001 eV/atom. From the analysis of our results, we could relate k-point density and plane wave cut-off to material parameters such as density, the slope of bands, number of band-crossings, the maximum plane-wave cut-off used in pseudopotential generation, crystal systems, and the number of unique species in materials. We also identified some material species that would require more careful convergence than others. Moreover, we statistically investigated the dependence of k-points and cutoff on exchange-correlation functionals. We utilized all this data to train machine learning models to predict the k-point line density and plane-wave cut-off for generalized materials. This would provide users with a good starting point towards converged DFT calculations. The code used, and the converged data are available on the following websites: https://jarvis.nist.gov/, and https://github.com/usnistgov/jarvis .

References in corpus (14)

• Van der Waals density functionals applied to solids
• AFLOW: An automatic framework for high-throughput materials discovery
• High-accuracy first-principles determination of the structural, vibrational and thermodynamical properties of diamond, graphite, and derivatives
• Crystallographic Phase Transition and High-Tc Superconductivity in LaFeAsO:F
• The Role of the Basis Set: Assessing Density Functional Theory
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
• Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
• Elastic properties of bulk and low-dimensional materials using Van der Waals density functional
• High pressure structures of disilane and their superconducting properties
• The crystal structure of cold compressed graphite
• Efficiency of Generalized Regular k-point Grids
• Direct observation of the superconducting gap in phonon spectra
• Phonons and electron-phonon coupling in the phonon-mediated superconductor YNi2B2C
• Non-linear elastic effects in phase field crystal and amplitude equations: Comparison to ab initio simulations of bcc metals and graphene

Cited by in corpus (24)

• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• Rapid Generation of Optimal Generalized Monkhorst-Pack Grids
• Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
• High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• Uncertainty Prediction for Machine Learning Models of Material Properties
• Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
• Data-driven Discovery of 3D and 2D Thermoelectric Materials
• High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors
• Database of novel magnetic materials for high-performance permanent magnet development
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models
• Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• Systematic DFT+U and Quantum Monte Carlo benchmark of magnetic two-dimensional (2D) CrX${}_3$ (X = I, Br, Cl, F)
• A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
• Density Functional Theory based Electric Field Gradient Database
• InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• Predicting Anomalous Quantum Confinement Effect in van der Waals Materials
• Automated optimization of convergence parameters in plane wave density functional theory calculations via a tensor decomposition-based uncertainty quantification
• $\beta$-As${}_2$Te${}_3$: Pressure-Induced 3D Dirac Semi-Metal
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics