Abstract Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.
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Mechanisms of Interface Cleaning in Heterostructures Made from Polymer‐Contaminated Graphene
Heterostructures obtained from layered assembly of 2D materials such as graphene and hexagonal boron nitride have potential in the development of new electronic devices. Whereas various materials techniques can now produce macroscopic scale graphene, the construction of similar size heterostructures with atomically clean interfaces is still unrealized. A primary barrier has been the inability to remove polymeric residues from the interfaces that arise between layers when fabricating heterostructures. Here, the interface cleaning problem of polymer‐contaminated heterostructures is experimentally studied from an energy viewpoint. With this approach, it is established that the interface cleaning mechanism involves a combination of thermally activated polymer residue mobilization and their mechanical actuation. This framework allows a systematic approach for fabricating record large‐area clean heterostructures from polymer‐contaminated graphene. These heterostructures provide state‐of‐the‐art electronic performance. This study opens new strategies for the scalable production of layered materials heterostructures.
more » « less- Award ID(s):
- 1940764
- NSF-PAR ID:
- 10445164
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Small
- Volume:
- 18
- Issue:
- 20
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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