High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

Abstract

In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17 506 surface energies and 1000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites: www.ctcms.nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis. Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

“…Despite their successes, existing DFT databases face limitations due to issues intrinsic to conventional DFT approaches, e.g., the generalized gradient approximation of Perdew-Burke-Ernzerhof (GGA-PBE) 21,22 . Drawbacks of the existing DFT databases include non-inclusion of van der Waals (vdW) interactions 6 , bandgap underestimations 23 , non-inclusion of spinorbit coupling 5 , overly simplifying magnetic ordering 24 , neglecting defects 25 (point, line, surface and volume), unconverged computational parameters such as k-points 26 , ignoring temperature effects 27 (generally DFT calculations are performed at 0 K), lack of layer/ thickness-dependent properties of low dimensional materials 28 , and lacking interfaces/heterostructures of materials 29 , all of which can be critical for realistic material-applications. In addition, there are several other computational approaches, such as classical force-field (FF) 30 , computational microscopy, phase-field (PF), CALculation of PHAse Diagrams (CALPHAD) 31 , and Orientation Distribution Functions (ODF) 32 which lack the integrated tools and databases that have been developed for DFT-based computational approaches.…”

“…Started in 2017, JARVIS-DFT 5,6,[23][24][25]28,29,45,49,50 is a repository based on DFT calculations that mainly uses the vdW-DF-OptB88 van der Waals functional 51 . The database also uses beyond-GGA approaches for a subset of materials, including the Tran-Blaha modified Becke-Johnson (TBmBJ) meta-GGA 52 , the hybrid functional PBE0, the hybrid range-separated functional Heyd-Scuseria-Ernzerhof (HSE06), Dynamical Mean Field Theory (DMFT), and G 0 W 0 .…”

“…The JARVIS-FF 25,53 database, also started in 2017, is a repository of classical force-field/potential computational data intended to help a user select the most appropriate force-field for a specific application. Many classical force-fields are developed for a particular set of properties (such as energies), and may not have been tested for properties not included in training (such as elastic constants, or defect formation energies).…”

High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses

“…Applications of these computational techniques in a highthroughput manner have led to several databases of computed geometries and many physicochemical properties, AFLOW 4 , Materials-project 3 , Khazana 17 , Open Quantum Materials Database (OQMD) 5 , NOMAD 7 , Computational Materials Repository (CMR) 39 , NIMS databases 40 , and our NIST-JARVIS databases 6,8,[21][22][23][41][42][43][44][45][46][47] . Despite a few systematic experimental databases of IR data (such as https://webbook.nist.gov/chemistry/vib-ser/), a systematic investigation of IR for inorganic materials is still lacking.…”

Density functional theory-based electric field gradient database

“…The Materials Genome Initiative (MGI) 19 (https://mgi.gov/) based projects such as AFLOW 20 , Materials-project 21 , Khazana 17 , Open Quantum Materials Database (OQMD) 22 , NOMAD 23 , Computational Materials Repository (CMR) 24 , NIMS 25 (https://crystdb.nims.go.jp/en/) and NIST-JARVIS [26][27][28][29][30][31][32][33][34][35][36][37][38] have played key roles in the generation of electronic-property related databases, and it is an obvious next step to extend them to nuclear physics-related quantities such as EFGs. There has been some systematic experimental database development in the past such as Japan Association for International Chemical Information (JAICI) Nuclear Quadrupole Resonance Spectrum (NQRS) database 39,40 (https://www.jaici.or.jp/wcas/wcas_nqrs.htm) which hosted NQR/ NMR data with specific compound-related data for hundreds of materials but has now gone offline for public usage.…”