About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
Presentation Title |
Multi-Fidelity Machine Learning for Perovskite Discovery |
Author(s) |
Arun Kumar Mannodi Kanakkithodi |
On-Site Speaker (Planned) |
Arun Kumar Mannodi Kanakkithodi |
Abstract Scope |
The ABX3 perovskite crystal structure is ubiquitous and the subject of extensive study owing to the sheer tunability of electronic and optical properties that can be achieved. The discovery of novel perovskite compositions, including complex alloys with attractive properties, is hindered by the combinatorial nature of the chemical space and a general lack of quantification of systematic inaccuracies in simulations such as from first principles density functional theory (DFT). In this work, we combine large datasets of DFT computed stability, band gaps, optical absorption, and defect formation energies of halide perovskites from various functionals with smaller quantities of corresponding experimental measurements, collected from the literature and generated at UCSD, and train multi-fidelity machine learning models to make property predictions at experimental accuracy. Such predictions are sequentially improved and coupled with a recommendation engine for new computations and experiments to gradually achieve new stable compositions with targeted band gap and absorption. |