1 citations · 2 across the 2 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025★ 1 cited
In context learning Foundation models for Materials Property Prediction with Small datasets
Qinyang Li, Rongzhi Dong, Nicholas Miklaucic +6
Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context lea…
cond-mat.mtrl-sci2025★ 1 cited
Machine Learning-Guided Discovery of Temperature-Induced Solid-Solid Phase Transitions in Inorganic Materials
Cibrán López, Joshua Ojih, Ming Hu +3
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical…
cond-mat.mtrl-sci2025
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…