Amorphous materials & van der Waals Heterostructures studied with AI-based multiscale approaches for advancing innovation in interconnects, spintronics, neuromorphic computing and more

Event date and time: 21/02/2025 10:00 am

Event location: Seminar Room NIMP

GENERAL SEMINAR: Prof. Stephan Roche, ICREA Institucio Catalana de Recerca i Estudis Avancats, Barcelona, Spain

ICREA Prof. Stephan Roche is working at the Catalan Institute of Nanosciences and Nanotechnology (ICN2) and BIST. He leads the Theoretical and Computational Nanoscience group which focuses on physics of Dirac materials (graphene and topological insulators) and 2D materials-based van der Waals heterostructures. He pioneered the development of linear scaling quantum transport approaches enabling simulations of billion atoms-scale disordered models (www.lsquant.org). He studied Theoretical Physics at ENS and got a PhD (1996) at Grenoble University (France); worked in Japan, Spain & Germany; was appointed as assistant Prof. in 2000, CEA Researcher in 2004 and joined ICREA in 2009. He received the Friedrich Wilhelm Bessel prize from the Alexander von Humboldt Foundation (Germany). From 2013 till 2023, he has been very active in the Graphene Flagship, as leader of the work package SPINTRONICS and as DIVISION leader. Finally, he is leader and coordinator of the “Quantum Communications” activities at ICN2.

Abstract

Amorphous materials such as boron nitride (aBN) and amorphous graphene (aG) are becoming prominent materials for many different applications due to their good properties such as thermal stability, mechanical properties, insulating behavior, and ultralow dielectric constant in aBN (<2). Moreover, amorphous films are more suitable to large area deposition compared to clean hBN or graphene since it can be grown at low temperatures (about 400 ºC) and on various substrates [1-3]. However, their properties depend on the nature and degree of disorder, which needs a well-defined metrics for benchmarking materials. Having such metrics in place will allow to tune the properties and performance of these films during the fabrication for desired applications. In this context, revealing the relationship between fabrication strategies and the material properties of the film is also crucial.

Capturing the key features of the amorphous nature of materials requires theoretical characterization to understand how material properties change with the microstructure. Since simulations of amorphous materials need large structural models, density functional theory (DFT) is not a suitable tool despite the high accuracy it offers. On the other hand, molecular dynamics (MD) simulations with empirical interatomic potentials require much less computational cost; however, they are not accurate enough to correctly describe the local environment of amorphous materials. Machine learning-driven interatomic potentials (ML-IP) can describe the local environment with a similar accuracy to DFT and at a much lower cost [4,5]. Here, using machine learning techniques [6-8], we present a systematic analysis to screen out possible realistic morphologies as a function of growth parameters, such as temperature, quenching rate, and the presence of a dopant, and their corresponding material properties. The extensive simulations of a large quantity of possible structures will guide experimental research and provide trends of scaling behavior as a function of experimentally controllable parameters. The impact of amorphousness on dielectric properties will be also discussed for aBN and aG [9,10].

Then, we will introduce current challenges in using van der Waals heterostructures and quantum materials for applications in spintronics, neuromorphic computing and quantum technologies, with again a focus on the necessity to develop smart scientific workflows, massively harnessing AI tools for accelerating innovation and industrial impact [11,12,13].

Figure : Typical amorphous structure of aBN:C compounds, namely an amorphous BN structure with a certain density of carbon atoms, affecting the overall system properties (thermal stability, mechanical and dielectric properties, etc)

1.References

[1] Hong, S et al. Nature 582, 511–514 (2020).

[2] Glavin, N. R., et al., Adv. Func. Mat. (2016).

[3] Chen, C. Y., et al., arxiv: 2312.09136 (cond-mat).

[4] Unruh, D., Meidanshahi, RZ., Goodnick, SM., Csányi, G. and Zimányi GT. Physical Review Materials 6, 065603 (2022).

[5] Deringer, VL. and Csányi, G. Physical Review B 95, 094203 (2017).

[6] Kaya, O., et al. Nanoscale Horizons 8, 361–367 (2023).

[7] Kaya, O. et al. J. Phys. Mater. 7 025010 (2024).

[8] Kaya, O. et al. arXiv:2402.01251 (cond-mat).

[9] Th. Galvani et al. Journal of Physics: Materials 7 (3), 035003 (2024)

[10] Th. Galvani, O. Kaya, S. Roche, unpublished

[11] JF Sierra, J Fabian, RK Kawakami, S Roche, SO Valenzuela, Nature Nanotechnology 16 (8), 856-868 (2021)

[12] H Yang, SO Valenzuela, M Chshiev, S Couet, B Dieny, B Dlubak, A Fert, et al. S. Roche, Nature 606 (7915), 663-673 (2022)

[13]MA Villena, O Kaya, U Schwingenschlögl, S Roche, M Lanza, Materials Science and Engineering: R: Reports 160, 100825 (2024)


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