Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with $10^4$ data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.Comment: 6+9 pages, 3+6 figure

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.Comment: 22 + 7 pages, 6 + 5 figure

Correcting low-frequency noise with continuous measurement

Low-frequency noise presents a serious source of decoherence in solid-state qubits. When combined with a continuous weak measurement of the eigenstates, the low-frequency noise induces a second-order relaxation between the qubit states. Here we show that the relaxation provides a unique approach to calibrate the low-frequency noise in the time-domain. By encoding one qubit with two physical qubits that are alternatively calibrated, quantum logic gates with high fidelity can be performed.Comment: 10 pages, 3 figures, submitte

Application of Hybrid Fillers for Improving the Through-Plane Heat Transport in Graphite Nanoplatelet-Based Thermal Interface Layers.

The in-plane alignment of graphite nanoplatelets (GNPs) in thin thermal interface material (TIM) layers suppresses the though-plane heat transport thus limiting the performance of GNPs in the geometry normally required for thermal management applications. Here we report a disruption of the GNP in-plane alignment by addition of spherical microparticles. The degree of GNP alignment was monitored by measurement of the anisotropy of electrical conductivity which is extremely sensitive to the orientation of high aspect ratio filler particles. Scanning Electron Microscopy images of TIM layer cross-sections confirmed the suppression of the in-plane alignment. The hybrid filler formulations reported herein resulted in a synergistic enhancement of the through-plane thermal conductivity of GNP/Al2O3 and GNP/Al filled TIM layers confirming that the control of GNP alignment is an important parameter in the development of highly efficient GNP and graphene-based TIMs

Topology of Entanglement in Multipartite States with Translational Invariance

The topology of entanglement in multipartite states with translational invariance is discussed in this article. Two global features are foundby which one can distinguish distinct states. These are the cyclic unit and the quantised geometric phase. Furthermore the topology is indicated by the fractional spin. Finally a scheme is presented for preparation of these types of states in spin chain systems, in which the degeneracy of the energy levels characterises the robustness of the states with translational invariance.Comment: major revision. accepted by EPJ