16 citations · 37 across the 22 of their papers we have counts for
4 papers · 2 filters
Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction
Nihang Fu, Sadman Sadeed Omee, Jianjun Hu
Deep learning (DL) models have been widely used in materials property prediction with great success, especially for properties with large datasets. However, the out-of-distribution…
CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong +6
Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and sig…
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong +2
In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is…
Physics guided dual Self-supervised learning for structure-based materials property prediction
Nihang Fu, Lai Wei, Jianjun Hu
Deep learning (DL) models have now been widely used for high-performance material property prediction for properties such as formation energy and band gap. However, training such D…