Aria Mansouri Tehrani
Materials design, machine learning, solid-state chemistry, and DFT researcher
Summary
Work
Education
Writing
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
January 1, 2025A comprehensive account of AI for quantum, atomistic, and continuum systems, focusing on how AI advances natural sciences by improving and accelerating the understanding of natural phenomena across various spatial and temporal scales.
Machine Learning Directed Search for Ultraincompressible, Superhard Materials
July 1, 2018Developed a machine-learning model to direct the synthetic efforts toward compounds with high hardness by predicting elastic moduli. The research involved synthesizing ternary rhenium tungsten carbide and quaternary molybdenum tungsten borocarbide, confirming their ultraincompressible nature and superhard properties.
Identifying an efficient, thermally robust inorganic phosphor host via machine learning
January 1, 2018Merged a support vector machine regression model with high-throughput density functional theory calculations to identify efficient and thermally robust rare-earth substituted inorganic phosphors for LED lighting. This led to the identification of NaBaB9O15:Eu2+ with high quantum yield and excellent thermal stability.
Predicting the band gaps of inorganic solids by machine learning
January 1, 2018Pioneering work in applying machine learning to predict band gaps of inorganic solids, contributing to materials design and discovery.