204 citations · 555 across the 12 of their papers we have counts for
12 papers
What drives performance in molecular MPNNs? An operator-level factorial benchmark
Panyu Jiao, Shuizhou Chen, Yiheng Shen +3
Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how speci…
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Zhonglin Cao, Yuyang Wang, Cooper Lorsung +1
Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electroosmotic flow. Due to the non-negligible surfac…
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
Yuyang Wang, Changwen Xu, Zijie Li +1
Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) appro…
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…
Graph Neural Networks for Molecules
Yuyang Wang, Zijie Li, Amir Barati Farimani
Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs…
TransPolymer: a Transformer-based language model for polymer property predictions
Changwen Xu, Yuyang Wang, Amir Barati Farimani
Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are requ…
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…
On Thermodynamic Interpretation of Copula Entropy
Jian Ma
Copula Entropy (CE) is a recently introduced concept for measuring correlation/dependence in information theory. In this paper, the theory of CE is introduced and the thermodynamic…
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Yuyang Wang, Zhonglin Cao, Amir Barati Farimani
Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and geometry optimization of nanopores on…
Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization
Yuyang Wang, Kenji Shimada, Amir Barati Farimani
The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the pri…