| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Machine Learning Approaches for Improving Density Functional Tight Binding Models of Reactive Materials: Application to Astrobiolgical Materials and Surface Chemistry |
| Author(s) |
Nir Goldman |
| On-Site Speaker (Planned) |
Nir Goldman |
| Abstract Scope |
Density Functional Tight Binding (DFTB) methods can accurately probe chemical reactivity at nanosecond timescales but can be challenging to parameterize for each system of interest due to the different bonding types that can occur. Here, we have created a machine learning-based approach for determining the DFTB repulsive energy which is both rapidly optimized, systematically improvable, and highly transferable. Our method leverages the Chebyshev Interaction Model for Efficient Simulation (ChIMES), a reactive many-body molecular dynamics force field where interactions are represented by linear combinations of Chebyshev polynomials. We have created ChIMES/DFTB models for a wide variety of systems, including potential hydrogen storage materials and impacting astrophysical ices and the synthesis of life-building compounds. Our approach is easy to implement and can yield accurate DFTB models for a number of challenging materials and conditions where chemical properties can be difficult to model with standard quantum approaches alone. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |