16 citations · 37 across the 22 of their papers we have counts for
4 papers · 1 filter
Generative Design of inorganic compounds using deep diffusion language models
Rongzhi Dong, Nihang Fu, dirisuriya M. D. Siriwardane +1
Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting criteria such as char…
Improving realistic material property prediction using domain adaptation based machine learning
Jeffrey Hu, David Liu, Nihang Fu +1
Materials property prediction models are usually evaluated using random splitting of datasets into training and test datasets, which not only leads to over-estimated performance du…
MD-HIT: Machine learning for materials property prediction with dataset redundancy control
Qin Li, Nihang Fu, Sadman Sadeed Omee +1
Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials re…
Global mapping of structures and properties of crystal materials
Qinyang Li, Rongzhi Dong, Nihang Fu +3
Understanding material composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies fo…