1 citations · 1 across the 2 of their papers we have counts for
5 papers
From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discovery
Iman Peivaste, Salim Belouettar
Discovering high-entropy alloy (HEA) compositions that reliably form a target crystal phase is a high-dimensional inverse design problem that conventional trial-and-error experimen…
Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
Iman Peivaste, Nicolas D. Boscher, Ahmed Makradi +1
Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, c…
ChemNavigator: Agentic AI Discovery of Design Rules for Organic Photocatalysts
Iman Peivaste, Ahmed Makradi, Salim Belouettar
The discovery of high-performance organic photocatalysts for hydrogen evolution remains limited by the vastness of chemical space and the reliance on human intuition for molecular…
Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
Iman Peivaste, Salim Belouettar, Francesco Mercuri +15
Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material desig…
Teaching Artificial Intelligence to Perform Rapid, Resolution-Invariant Grain Growth Modeling via Fourier Neural Operator
Iman Peivaste, Ahmed Makradi, Salim Belouettar
Microstructural evolution, particularly grain growth, plays a critical role in shaping the physical, optical, and electronic properties of materials. Traditional phase-field modeli…