| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Materials Design through AI Composition and Process Optimization
|
| Presentation Title |
AI-driven Discovery of Novel High Entropy Semiconductor Alloys |
| Author(s) |
Arun Kumar Mannodi Kanakkithodi, Xueying Li, David Fenning, Maria K.Y. Chan |
| On-Site Speaker (Planned) |
Arun Kumar Mannodi Kanakkithodi |
| Abstract Scope |
High-entropy alloys in semiconductor chemical spaces resulting from arbitrary mixing at cation or anion sites can help enhance the stability, optical absorption, electronic properties and performance of materials for applications such as solar cells, infrared and quantum sensors, and electronics. In this work, we develop a general AI-based framework for the on-demand prediction of the structure, formation energy, band gap, optical absorption and defect behavior of high entropy alloys belonging to semiconductor classes of interest in photovoltaic and related optoelectronic applications. This framework is powered by high-throughput quantum mechanical computations, unique descriptors ranging from atomic coordination environments to elemental properties to low-fidelity computational outputs, and the rigorous training of advanced neural network-based predictive and optimization models. AI-based recommendations are synergistically coupled with targeted synthesis and characterization, leading to the successful validation and discovery of novel compositions for improved performance in solar cells. |