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We consider feature representation learning problem of molecular graphs. Graph Neural Networks have been widely used in feature representation learning of molecular graphs. However, most existing methods deal with molecular graphs individually while neglecting their connections, such as motif-level relationships. We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue. In particular, we build a heterogeneous motif graph that contains motif nodes and molecular nodes. Each motif node corresponds to a motif extracted from molecules. Then, we propose a Heterogeneous Motif Graph Neural Network (HM-GNN) to learn feature representations for each node in the heterogeneous motif graph. Our heterogeneous motif graph also enables effective multi-task learning, especially for small molecular datasets. To address the potential efficiency issue, we propose to use an edge sampler, which can significantly reduce computational resources usage. The experimental results show that our model consistently outperforms previous state-of-the-art models. Under multi-task settings, the promising performances of our methods on combined datasets shed light on a new learning paradigm for small molecular datasets. Finally, we show that our model achieves similar performances with significantly less computational resources by using our edge sampler., International Conference on Machine Learning (2022)

We consider the explanation problem of Graph Neural Networks (GNNs). Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substructures, which are more important for graph data. The only method that considers subgraphs tries to search all possible subgraphs and identify the most significant subgraphs. However, the subgraphs identified may not be recurrent or statistically important. In this work, we propose a novel method, known as MotifExplainer, to explain GNNs by identifying important motifs, recurrent and statistically significant patterns in graphs. Our proposed motif-based methods can provide better human-understandable explanations than methods based on nodes, edges, and regular subgraphs. Given an input graph and a pre-trained GNN model, our method first extracts motifs in the graph using well-designed motif extraction rules. Then we generate motif embedding by feeding motifs into the pre-trained GNN. Finally, we employ an attention-based method to identify the most influential motifs as explanations for the final prediction results. The empirical studies on both synthetic and real-world datasets demonstrate the effectiveness of our method.

Thermal emission is the radiation of electromagnetic waves from hot objects. The promise of thermal-emission engineering for applications in energy harvesting, radiative cooling, and thermal camouflage has recently led to renewed research interest in this topic. There is a substantial need for accurate and precise measurement of thermal emission in a laboratory setting, which can be challenging in part due to the presence of background emission from the surrounding environment and the measurement instrument itself. This is especially true for measurements of emitters at temperatures close to that of the environment, where the impact of background emission is relatively large. In this paper, we describe, recommend, and demonstrate general procedures for thermal-emission measurements that are applicable to most experimental conditions, including less-common and more-challenging cases that include thermal emitters with temperature-dependent emissivity and emitters that are not in thermal equilibrium., Comment: Main text + Appendices

Accurate characterization of thermal emitters can be challenging due to the presence of background thermal emission from components of the experimental setup and the surrounding environment. This is especially true for an emitter operating close to room temperature. Here, we explore characterization of near-room-temperature thermal emitters using Fourier-transform infrared (FTIR) spectroscopy. We find that the thermal background arising from optical components placed between the beam splitter and the detector in an FTIR spectrometer appears as a "negative" contribution to the Fourier-transformed signal, leading to errors in thermal-emission measurements near room temperature. Awareness of this contribution will help properly calibrate low-temperature thermal-emission measurements., Main text and supplementary info

We demonstrate a metamaterial that cannot be described by a single set of refractive-index and impedance values, even for fixed frequency and polarization. The metamaterial structure is a stack of dissimilar waveguides with subwavelength width and spacing, which guide light at different phase velocities. This multi-refractive-index metamaterial (MRIM) can be viewed as a spatial superposition of multiple homogeneous materials. Using full-wave simulations, we demonstrate several optical components based on MRIMs, including prisms that deflect light to multiple angles, lenses with multiple focal points, and multi-index Fabry-Perot etalons with an enhanced density of resonant modes. We provide a theoretical framework for analyzing MRIMs to determine effective refractive indices, fractions of power to each channel (i.e., to each refractive index), and transmittance., Comment: Main text and supplementary info

We demonstrate a smooth and low loss silver (Ag) optical superlens capable of resolving features at 1/12th of the illumination wavelength with high fidelity. This is made possible by utilizing state-of-the-art nanoimprint technology and intermediate wetting layer of germanium (Ge) for the growth of flat silver films with surface roughness at sub-nanometer scales. Our measurement of the resolved lines of 30nm half-pitch shows a full-width at half-maximum better than 37nm, in excellent agreement with theoretical predictions. The development of this unique optical superlens lead promise to parallel imaging and nanofabrication in a single snapshot, a feat that are not yet available with other nanoscale imaging techniques such as atomic force microscope or scanning electron microscope.

In this paper we proposed a novel overlay alignment method using two sets of identical photonic crystals (PhCs). In this method the reflection or transmission spectrum of the two overlaid photonic crystals is measured to help wafer tilt, yaw rotation, and translation aligning. The initial testing results with two 1D photonic crystals and analysis of the alignment accuracy are presented. This method is particularly useful in building photonic crystal stacks with nanoimprint lithography (NIL)., 13pages 6 figures