306 citations · 387 across the 6 of their papers we have counts for
6 papers
MoMa: A Modular Deep Learning Framework for Material Property Prediction
Botian Wang, Yawen Ouyang, Yaohui Li +8
Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fai…
Learning Harmonic Molecular Representations on Riemannian Manifold
Yiqun Wang, Yuning Shen, Shi Chen +3
Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the pre…
Learning the Crystal Structure Genome for Property Classification
Yiqun Wang, Xiao-Jie Zhang, Fei Xia +3
Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structu…
Featureless adaptive optimization accelerates functional electronic materials design
Yiqun Wang, Akshay Iyer, Wei Chen +1
Electronic materials exhibiting phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are chal…
Assessing exchange-correlation functional performance in the chalcogenide lacunar spinels GaMQ (M = Mo, V, Nb, Ta; Q = S, Se)
Yiqun Wang, Danilo Puggioni, James M. Rondinelli
We perform systematic density functional theory (DFT) calculations to assess the performance of various exchange-correlation potentials in describing the chalcogenide GaM$…
Symbolic Regression in Materials Science
Yiqun Wang, Nicholas Wagner, James M. Rondinelli
We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic progra…