Dataset

JARVIS C2DB

  Description

The JARVIS-C2DB dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This subset contains configurations from the Computational 2D Database (C2DB), which contains a variety of properties for 2-dimensional materials across more than 30 differentcrystal structures. JARVIS is a set of tools and datasets built to meet current materials design challenges.Additional details stored in dataset… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/JARVIS_C2DB.

We present a novel target-driven methodology devised to predict the Heyd–Scuseria–Ernzerhof (HSE) band gap of two-dimensional (2D) materials leveraging the comprehensive C2DB database. This innovative approach integrates machine learning and density functional theory (DFT) calculations to predict the HSE band gap, conduction band minimum (CBM), and valence band maximum (VBM) of 2176 types of 2D materials. Subsequently, we collected a comprehensive data set comprising 3539 types of 2D materials, each characterized by its HSE band gaps, CBM, and VBM. Considering the lattice disparities between MoSi2N4 (MSN) and 2D materials, our analysis predicted 766 potential MSN/2D heterostructures. These heterostructures are further categorized into four distinct types based on the relative positions of their CBM and VBM: Type I encompasses 230 variants, Type II comprises 244 configurations, Type III consists of 284 permutations, and 0 band gap comprises 8 types.

Chakshu/test-7fc14b13-c2db-4706-9fe2-494d2db987fe dataset hosted on Hugging Face and contributed by the HF Datasets community

Please note that the C2DB dataset is available upon request. Contact the corresponding author of the C2DB dataset to obtain the files, including relaxed.db and unrelaxed.db. After successfully requesting the C2DB dataset, process it using `convert_c2db.py` available in this repository.

Data fromhttps://cmrdb.fysik.dtu.dk/c2db/converted to jarvis.core.Atoms format for ML training purposes.

TCSP 2.0 templte database, it includes the Materials Project (MP) database, Materials Cloud database (both 2D and 3D), The Computational 2D Materials Database (C2DB), and Graph Networks for Materials Science database(GNoME).

The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 315 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. This repository then contains the main source of data generated in this work, which encompasses full information regarding the materials structure and band structure calculations results, and a database of all the identified spin splittings for these compounds, available in multiple formats.

Auxetic two-dimensional (2D) crystals that yield a negative Poisson’s ratio (NPR) exhibit unusual transverse expansion when stretched. Such materials have great potential for electronic skins, energy harvesting, mechatronic shape display devices, and strain sensors. However, most 2D crystals have positive Poisson’s ratios, and the number of existing 2D materials with NPR is limited. Accelerating the discovery of these rare auxetic materials remains a long-term challenge. Utilizing high-throughput computations, this work discovers 108 stable 2D crystals with negative in-plane Poisson’s ratios that could be successfully fabricated from the 4047 2D crystals in the Computational 2D Materials Database (C2DB). We find 4 crystal types with the potential for significant auxetic effect regardless of the orientations, that is, MX2O8, MX2O6, MX3, and MXX′. The auxetic effects for the former two are simply from crystal geometry, while further first-principles calculations based on MX3 (M = Sc, Ti, V, Cr, ... and X = Cl, Br, I) and MXX′ (M = Mn, Fe, Co, Ni; X = Li, Na, Mg, Ca, Sr; and X′ = Si, Ge, P, As, Sb) reveal that the NPR originates from both the crystal geometry and electronic structures. Because some databases lack the information of elasticity and stability, deep learning models are trained using C2DB and employed to find 92 other stable 2D crystals with NPR. The band gaps of the 200 stable auxetic 2D crystals discovered in our work cover a wide range from 0 to 5.7 eV, which provide abundant candidates for nanoscale electronics with intriguing physicochemical properties.

gkm-SVM-model - TF_2123_hg38: gkm-SVM model for NUFIP1 ChIP-seq in K562 (human) - ENCODE - U01HG009380 - Michael Beer, JHU

Structure graphs of 2D materials from C2DB (Haastrup et al., 2D Materials 5, 042002 (2018)).