31 citations · 36 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
Accelerating phase-field-based simulation via machine learning
Iman Peivaste, Nima H. Siboni, Ghasem Alahyarizadeh +4
Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they s…