Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature

Abstract

2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets.

When Machine Learning Meets 2D Materials: A Review

“…[ 15 ] Most recently, in January 2023, Song et al established a framework that enables the identification of 2D van der Waals (vdW) magnets with high probability for experimental verification, based on a large body of literature in materials science. [ 16 ] As shown in Figure 1b , the number of published articles per year related to ML in 2D materials research is increasing steadily, and this trend is likely to continue. To provide further insight in ML‐enabled advances, Figure 1c categorizes the publications based on the prediction, discovery, preparation, characterization, and fundamental research of 2D materials.…”

“…[ 9 ] Reproduced with permission. [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ] Copyright 2018, American Chemical Society; 2018, Springer Nature; 2019, Elsevier; 2020, American Chemical Society; 2021, Springer Nature; 2022, IOP Publishing; 2023, John Wiley and Sons. b) Number of publications of ML‐enabled studies on 2D materials.…”

“…a) Major breakthroughs in 2D materials enabled by ML-based approaches [9]. Reproduced with permission [10][11][12][13][14][15][16]. Copyright 2018, American Chemical Society; 2018, Springer Nature; 2019, Elsevier; 2020, American Chemical Society; 2021, Springer Nature; 2022, IOP Publishing; 2023, John Wiley and Sons.…”

Ultralow Contact Resistance and Efficient Ohmic Contacts in MoGe2P4–Metal Contacts

“…Two-dimensional (2D) semiconductors, such as graphene, transition metal dichalcogenides (TMDs), and black phosphorene (BP), have exhibited potential applications in field effect transistors (FETs). − However, their practical applications still have some limitations, such as the zero-band gap in graphene, , poor environmental stability in BP, , and low carrier mobility in TMDs. , MoSi 2 N 4 and WSi 2 N 4 monolayers, recently synthesized by the chemical vapor deposition (CVD), have shown the potential to realize high-speed and low-power consumption devices due to the suitable band gap, high carrier mobility, and outstanding environmental stability. − Another MoSi 2 N 4 family material, the MoGe 2 P 4 monolayer, has attracted much attention due to its suitable band gap (0.5 eV) and outstanding carrier mobility (exceeding 10 3 cm 2 V –1 s –1 ), which is higher than those of the recently reported MoSi 2 N 4 /WSi 2 N 4 . Besides, the MoGe 2 P 4 monolayer is both dynamically and mechanically stable, which is much better than that of black phosphorene .…”

Emerging 2D Cobalt Telluride (CoxTey): from Theory to Applications

“…2D van der Waals (vdW) magnets with intrinsic magnetism attract increasingly interests from multi-disciplinary research communities [1,2] and become a versatile platform for both probing fundamental phenomena and propagating new device applications. [3,4] Hundreds of vdW magnets were theoretically predicted through high-throughput computation screening and machine learning, [5,6,7,8,9] but the family of newly developed DOI: 10.1002/adfm.202310372 2D magnets is relatively limited from the experimental perspective due to the difficulty in controlled synthesis and avoiding air degradation. [10,11,12,13] Among various fantastic candidates, cobalt telluride (Co x Te y ) widely reputed as star catalysts for hydrogen/oxygen evolution reactions, [14,15] water splitting, [16] photo-/electro-catalytic chemistry, [17,18,19] and even energy storage devices, [20,21] was re-recognized with tunable ferromagnetism and paramagnetism and is potential for memory and spintronic devices.…”