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

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

Mathematical Modeling of Product Rating: Sufficiency, Misbehavior and Aggregation Rules

Many web services like eBay, Tripadvisor, Epinions, etc, provide historical product ratings so that users can evaluate the quality of products. Product ratings are important since they affect how well a product will be adopted by the market. The challenge is that we only have {\em "partial information"} on these ratings: Each user provides ratings to only a "{\em small subset of products}". Under this partial information setting, we explore a number of fundamental questions: What is the "{\em minimum number of ratings}" a product needs so one can make a reliable evaluation of its quality? How users' {\em misbehavior} (such as {\em cheating}) in product rating may affect the evaluation result? To answer these questions, we present a formal mathematical model of product evaluation based on partial information. We derive theoretical bounds on the minimum number of ratings needed to produce a reliable indicator of a product's quality. We also extend our model to accommodate users' misbehavior in product rating. We carry out experiments using both synthetic and real-world data (from TripAdvisor, Amazon and eBay) to validate our model, and also show that using the "majority rating rule" to aggregate product ratings, it produces more reliable and robust product evaluation results than the "average rating rule".Comment: 33 page

Hybrid Density Functional Study of Structural and Electronic Properties of Functionalized \ce{Ti_{n+1}X_n} (X= C, N) monolayers

Density functional theory simulations with conventional (PBE) and hybrid (HSE06) functionals were performed to investigate the structural and electronic properties of MXene monolayers, \ce{Ti_{n+1}C_n} and \ce{Ti_{n+1}N_n} ($n$ = 1--9) with surfaces terminated by O, F, H, and OH groups. We find that PBE and HSE06 give similar results. Without functional groups, MXenes have magnetically ordered ground states. All the studied materials are metallic except for \ce{Ti_{2}CO_{2}}, which we predict to be semiconducting. The calculated density of states at the Fermi level of the thicker MXenes ($n$ $\geqslant$ 5) is much higher than for thin MXenes, indicating that properties such as electronic conductivity and surface chemistry will be different. In general, the carbides and nitrides behave differently with the same functional groups.Comment: 8 figure

The spin-polarized ν=0\nu=0 state of graphene: a spin superconductor

We study the spin-polarized $\nu=0$ Landau-level state of graphene. Due to the electron-hole attractive interaction, electrons and holes can bound into pairs. These pairs can then condense into a spin-triplet superfluid ground state: a spin superconductor state. In this state, a gap opens up in the edge bands as well as in the bulk bands, thus it is a charge insulator, but it can carry the spin current without dissipation. These results can well explain the insulating behavior of the spin-polarized $\nu=0$ state in the recent experiments.Comment: 6 pages, 4 figure