About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Computationally Accelerated Design of Dynamically Controlled Pathways of Growth for 2D Materials |
| Author(s) |
Soumendu Bagchi, Ryan Morelock, Zijie Wu, Simon Kim, Debangshu Mukherjee, Matthew Boeblinger, Matthew Brahlek, P. Ganesh |
| On-Site Speaker (Planned) |
Soumendu Bagchi |
| Abstract Scope |
Traditional approaches to bridge atomic scale dynamics with experimental observations at the microstructural level often rely on phenomenological models or force-fields of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under limited number of thermodynamical and kinetic drivers. This approach becomes particularly cumbersome to study synthesis and characterization of materials with complex dependencies on local environment. In this talk, we will demonstrate extremely scalable direct Bayesian sampling workflows that couple automated high-throughput ab initio and large-scale classical atomistic simulations. Applying a wide range of uncertainty quantification-driven parallel ensemble sampling algorithms for on-the-fly forcefield generation and tracking of directed structural transformation from amorphous to controlled crystalline phases in molecular dynamics, we will discuss how targeted synthesis of twisted 2D van der Waals layered materials can be achieved through guiding of multimodal experimental synthesis (or manipulation) platforms. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |