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

IMPROVED AMORPHOUS SOLID DISPERSION PERFORMANCE USING BINARY POLYMER COMBINATIONS

With increasing attrition rate of new molecular entities due to sub-optimum aqueou

Environmental and Genetic Origins of Hypertension: a life course perspective

The complicated etiology of hypertension is still not fully understood. By leveraging multiple data sources from the Netherlands and other countries, we investigated the influence of environmental and genetic factors on blood pressure from the perinatal period, to childhood and adolescence and into adulthood. The findings in this thesis provide insights into environmental and genetic influences on BP across the lifespan and thus may benefit early prevention of hypertension. First, early determinants including higher maternal prepregnancy BMI, maternal hypertension, relatively lower birth weight for gestational age, shorter gestational age, limited duration of breastfeeding, and more rapid early BMI gain are all related to higher childhood BP. Second, spouses show similarities for BP and hypertension in diverse populations. Third, adult-based genetic risk scores can predict BP levels and trajectories at an early age. Finally, larger GWASs in children and adults will help to identify more BP genes and develop more precise genetic predictors