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Abstract In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability required for material exploration. Given the critical need for high-throughput automated XRD pattern analysis, we present a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We also employ an expedited learning technique to refine our model’s expertise to experimental conditions. In addition, we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making. We evaluate our models using experimental data, materials unseen in training, and altered cubic crystals, where we observe state-of-the-art performance and even greater advances in space group classification. 

Abstract In alignment with the Materials Genome Initiative and as the product of a workshop sponsored by the US National Science Foundation, we define a vision for materials laboratories of the future in alloys, amorphous materials, and composite materials; chart a roadmap for realizing this vision; identify technical bottlenecks and barriers to access; and propose pathways to equitable and democratic access to integrated toolsets in a manner that addresses urgent societal needs, accelerates technological innovation, and enhances manufacturing competitiveness. Spanning three important materials classes, this article summarizes the areas of alignment and unifying themes, distinctive needs of different materials research communities, key science drivers that cannot be accomplished within the capabilities of current materials laboratories, and open questions that need further community input. Here, we provide a broader context for the workshop, synopsize the salient findings, outline a shared vision for democratizing access and accelerating materials discovery, highlight some case studies across the three different materials classes, and identify significant issues that need further discussion. Graphical abstract 

Formation of vitreous ice during rapid compression of water at room temperature is important for biology and the study of biological systems. Here, we show that Raman spectra of rapidly compressed water at greater than 1 GPa at room temperature exhibits the signature of high-density amorphous ice, whereas the X-ray diffraction (XRD) pattern is dominated by crystalline ice VI. To resolve this apparent contradiction, we used molecular dynamics simulations to calculate full vibrational spectra and diffraction patterns of mixtures of vitreous ice and ice VI, including embedded interfaces between the two phases. We show quantitatively that Raman spectra, which probe the local polarizability with respect to atomic displacements, are dominated by the vitreous phase, whereas a small amount of the crystalline component is readily apparent by XRD. The results of our combined experimental and theoretical studies have implications for detecting vitreous phases of water, survival of biological systems under extreme conditions, and biological imaging. The results provide additional insight into the stable and metastable phases of H 2 O as a function of pressure and temperature, as well as of other materials undergoing pressure-induced amorphization and other metastable transitions.