The document discusses a computational database aimed at accelerating the discovery of 2D and 3D materials, detailing various publications surrounding the identification and characterization of these materials using advanced computational techniques. It emphasizes the motivation to discover new low-dimensional materials, compare their properties with 3D materials and experimental data, and includes ongoing work related to machine learning and material property prediction. The database encompasses approximately 600 2D monolayers and 30,000 3D bulk materials, alongside a user-friendly web interface for accessing the information.

1. Computational Database for 2D and 3D materials to accelerate discovery
Publications
▪ “High-throughput Identification and Characterization of Two-dimensional
Materials using Density functional theory,” Scientific Reports 7, 5179 (2017).
▪ “Computational screening of high-performance optoelectronic materials using
OptB88vdW and TBmBJ formalisms “, accepted Scientific Data (2018).
▪ “Elastic properties of bulk and low-dimensional materials using OptB88vdW
functional in density functional theory”, submitted.
▪ “Machine learning with force-field inspired descriptors for materials: fast
screening and mapping energy landscape”, submitted.
▪ “Evaluation and comparison of classical interatomic potentials through a user-
friendly interactive web-interface,” Scientific Data 4, 160125 (2017)
K. Choudhary1, F. Tavazza1, F. Y. Congo1, A. C. E. Reid1, K. Garrity1, B. DeCost1, I. Kalish1, R. Beams1, S. Krylyuk1,
A. Davydov1, Z. Trautt1, M. W. Newrock1, Q. Zhang2 , S. Chowdhury2, N. V. Nguyen2, G. Cheon3, E. Reed3
1Materials Science and Engineering Division, National Institute of Standards and Technology, MD, USA
2Physical Measurement Laboratoty, National Institute of Standards and Technology, MD, USA
3Department of Materials Science and Engineering, Stanford University, Stanford, California, USA MML/MSED
• Enthalpy of formation and enthalpy of exfoliation
• Exfoliable materials: <200 meV/atom
Motivation
• Discover and characterize new low-dimensional
materials
• Compare with 3D materials
• Compare with experimental data whenever possible
Optoelectronic properties
Elastic properties
Energetics
Magnetic properties
Transport properties
• OptB88vdW, TBmBJ and HSE06 bandgaps
• Frequency dependent dielectric functions
• 6x6 elastic tensors, Poisson’s ratio and phonons
• Magnetic moments
• Carrier effective mass, Seebeck coefficient and zT
Dimensionality of materials
• ~ 600 2D monolayer & 30000 3D bulk materials
• Lattice constant criteria and data-mining approaches
On-going work
• Search for new topological insulators
• Machine learning for predicting material properties
1D and 0D materials
• Effect of exfoliation on low-D materials
Webpage: https://jarvis.nist.gov