Formidable progress has been recently achieved in the fabrication and characterization of disordered low dimensional materials with unprecedented properties. For instance, particular forms of disordered graphene (reduced graphene oxides) and other two-dimensional materials, obtained by chemical exfoliation techniques, have been found suitable to improve the performances of composite materials, with applications in energy, textiles, solar cells, thermoplastics etc. In parallel, catalytic vapor deposition methods have been used intensively to upscale the growth of two-dimensional materials at wafer size, with resulting material morphologies ranging from polycrystalline to fully amorphous phases. Recent reports on wafer-scale synthesis of ultrathin layered amorphous carbon and boron nitride, structurally dominated by sp2 hybridization but lacking crystalline orders, have shown superior performances of those materials as coating and interfacial components, providing a new platform for properties reinforcement in the industrial context.

Here, we present our computational strategies to achieve predictive modelling in this context. For the “computationally-assisted” material synthesis, we use machine learning to generate atomic interaction potential in an arbitrary disordered local environment and obtain realistic large-scale material configurations through classical molecular dynamics, while generic tight-binding models are generated based on resulting spatial atomic distribution and optimized DFT-inspired Hamiltonian parameters. Next, we employ efficient linear scaling quantum transport [1] (see also www.lsquant.org) to simulate structural models containing up to a billion atoms, hence demonstrating unprecedented supremacy to match predictive modelling with experimental scales, and therefore constituting decision-making tools for material design and industrial performances achievement. We illustrate our strategy on various examples including the (thermal) reduction process of graphene-oxides for recovering higher crystallinity, the correlation between the degree of disorder and resulting physical properties in polycrystalline materials [2], as well as amorphous phases of sp2 carbon members [3] and boron-nitride [4]. The obtained results provide scaling laws and guiding rules for researchers and engineers to target the proper material growth and integration conditions for reaching the upper limit of a given application objective, such as flexible conductors, spin channel, ultrathin heat spreaders, energy harvesters, insulating thermal barriers, or ultralow dielectric coefficient materials.
[1] Z. Fan et al. S. Roche, Physics Reports 903, 1-69 (2021)
[2] AW Cummings et al. Advanced Materials 26 (30), 5079-5094 (2014)
[3] A Antidormi, L Colombo, S Roche, 2D Materials 8 (1), 015028
[4] S. Hong, et al. A. Antidormi, S. Roche, M. Chhowalla, H.J. Shin & H.S. Shin, Nature 582, 511–514 (2020)

Multiscale Computational Methods for Exploring Electrical and Thermal properties of highly disordered Composites Materials: From Machine-learning based Molecular Dynamics to Linear Scaling Transport Methods

**Session Chair: **Yoshiyuki Kawazoe

SYM N - Multiscale Simulation, Defects, High Throughput Calculation, Magnetism

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Aleandro Antidormi

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Multiscale Computational Methods for Exploring Electrical and Thermal properties of highly disordered Composites Materials: From Machine-learning based Molecular Dynamics to Linear Scaling Transport Methods

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