Learning Topology-Specific Experts for Molecular Property Prediction

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.Comment: 11 pages with 8 figure

Plasmonic organic solar cell and its absorption enhancement analysis using cylindrical Ag nano-particle model based on finite difference time domain (FDTD)

We report the plasmon-assisted photocurrent enhancement in Ag nanoparticles (NPs)-embedded PEDOT:PSS/P3HT:PCBM organic solar cells, and theoretically investigate the causes of the improved optical absorption based on a cylindrical Ag-NPs model which is simulated with a finite difference time domain (FDTD) method. The proposed cylindrical Ag-NPs model is able to explain the optical absorption enhancement by the localized surface plasmon resonance (LSPR) modes, and to provide a further understanding of Ag-NPs shape parameters which play an important role to determine the broadband absorption phenomena in plasmonic organic solar cells

Absorption and transport enhancement by Ag nanoparticle plasmonics for organic optoelectronics

The organic films such as P3HT/PCBM incorporating Ag metal nanoparticles are fabricated and experimentally characterized. Due to the excited surface plasma induced by Ag metal nanoparticles, the absorption of the active organic material layer is increased by around 30%. The broadened absorption spectrum to the 260-650nm wavelength range is also observed from our measurements because of the enhanced scattering cross section by Ag metal nanoparticles. Furthermore, by incorporating Ag nanoparticles into the active layer, the mobility have also been improved. Finite Difference Time Domain (FDTD) simulations confirm the increase in transmission of electromagnetic radiation at visible wavelength. The hopping model is proposed to explain the transport mechanism for the device operations. These observations suggest a variety of approaches for improving the performance of general organic optoelectronic devices

Learning Topology-Specific Experts for Molecular Property Prediction

1