About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Machine Learned Models for Transition Metal Dichalcogenide |
Author(s) |
Henry Chan, Mathew Cherukara, Badri Narayanan, Subramanian Sankaranarayanan |
On-Site Speaker (Planned) |
Henry Chan |
Abstract Scope |
Transition metal dichalcogenide (TMD) are novel nanomaterials that can behave like conductors, semiconductors, or insulators depending on the type of transition metal used. With a thickness as small as three atoms and size dependent properties, TMDs have a great potential in applications such as flexible and wearable electronics. Despite the early discovery of TMDs and their synthesis via vapor deposition, fundamental understanding on their growth mechanisms remains largely unknown, which hindered the preparation of these materials on a larger scale. Molecular simulations can be used to address this problem, but the lack of accurate interatomic potentials and the large effort required to develop them presents a major barrier. Here, we demonstrate the use of a machine learning based framework in the development of a reactive force field model for tungsten diselenide. Our data-driven procedure led to models that accurately captures various properties, including structures, dynamical stability, phonon, and various energetics. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |