82 citations · 84 across the 4 of their papers we have counts for
4 papers
Predicting CO Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks
Yue Jian, Yuyang Wang, Amir Barati Farimani
Ionic Liquids (ILs) provide a promising solution for CO capture and storage to mitigate global warming. However, identifying and designing the high-capacity IL from the giant c…
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction
Zhonglin Cao, Rishikesh Magar, Yuyang Wang +1
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separatio…
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Rishikesh Magar, Yuyang Wang, Amir Barati Farimani
Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elus…
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
Yuyang Wang, Rishikesh Magar, Chen Liang +1
Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contras…