The synthesis of 2D materials has attracted significant attention in recent years due to their unique properties and potential applications in various fields such as electronics, energy, and catalysis. The stacked van der Waals heterostructures, in particular, are emerging as a prime candidates for quantum material design. However, the lack of controllable and reproducible synthesis methods is a significant hurdle to their industrial application. This is due to the lack of a comprehensive understanding of crucial growth mechanisms and the absence of real-time in-situ access to growth states for feedback process control. Experimental synthesis of these materials is often done by trial-and-error, leading to low reproducibility and controllability. The objective of this symposium is to bring together experts in the field to discuss theoretical, computational, and machine-learning methods for designing and synthesizing 2D materials beyond graphene.

Theoretical, computational, and machine learning methods and tools can assist and guide the design and synthesis of 2D materials beyond graphene. The symposium aims to focus on these methods at multiple length and time scales to provide a comprehensive understanding of growth mechanisms and enable real-time in-situ access to growth states, particularly it focuses on: (1) Nanoscale atomistic simulations, including density functional theory calculations and molecular dynamics simulations; (2) Mesoscale methods such as phase-field method, understanding the microstructure of 2D materials; (3) Macroscale continuum approaches, coupling thermal and chemical transport equations; (4) Machine learning models of growth and synthesis, providing predictive insights into the growth process.

2D materials atomic layer deposition chemical vapor deposition (CVD) (deposition) crystal growth microstructure multiscale nanostructure nucleation & growth physical vapor deposition (PVD)