| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Bridging the Electronic, Atomistic and Mesoscopic Scales using Machine Learning |
| Author(s) |
Subramanian Sankaranarayanan |
| On-Site Speaker (Planned) |
Subramanian Sankaranarayanan |
| Abstract Scope |
Molecular dynamics (MD) is a popular technique that has led to breakthrough advances in diverse fields, including tribology, energy storage, catalysis, sensing. The popularity of MD is driven by its applicability at disparate length/time-scales. Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales.
In this talk, I will present some of our recent work on the use of machine learning (ML) to seamlessly bridge the electronic, atomistic and mesoscopic scales for materials modeling. Our ML approach showed marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, hetero-interfaces to two-dimensional (2-D) materials and even water (arguably the most difficult system to capture from a molecular perspective). |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |