About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
2D Materials – Preparation, Properties, Modeling & Applications
|
| Presentation Title |
Computational and Data-Driven Strategies for Electronic and Catalytic Property Engineering in Two-Dimensional Materials |
| Author(s) |
Joshua Young |
| On-Site Speaker (Planned) |
Joshua Young |
| Abstract Scope |
2D materials offer a versatile platform for advancements in areas like nanoelectronics and heterogeneous catalysis owing to their highly tunable properties. This talk surveys our work in applying computational strategies, such as density functional theory, machine learning interatomic potentials (MLIPs), and data-driven machine learning models, for predicting and engineering these properties, often paired with experiment. On the electronic side, we discuss how chemical substitution and strain can induce and tune new properties in MXenes and transition metal dichalcogenides (such as ferroelectricity), as well as ongoing work using MLIPs and machine learning to screen 2D materials as next-generation dielectrics. In catalysis, we highlight the role of surface structure and composition in driving activity for CO2 reduction on 2D ferroelectrics, and combined computational-experimental studies of MXene-based electrocatalysts for pollutant degradation. This work highlights the range of computational tools available for accelerating the discovery of 2D materials with targeted electronic and catalytic properties. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Electronic Materials, Machine Learning |