Engineering topics

Billinge, Simon L.

Publications and source records attributed to Billinge, Simon L..

5 records

DOE OSTI2023

Dynamic crystallography reveals spontaneous anisotropy in cubic GeTe

Cubic energy materials such as thermoelectrics or hybrid perovskite materials are often understood to be highly disordered. In GeTe and related IV–VI compounds, this is thought to provide the low thermal conductivities needed for thermoelectric applications. Since conventional crystallography cannot distinguish between static disorder and atomic motions, we develop the energy-resolved variable-shutter pair distribution function technique. This collects structural snapshots with varying exposure times, on timescales relevant for atomic motions. In disagreement with previous interpretations, we find the time-averaged structure of GeTe to be crystalline at all temperatures, but with anisotropic anharmonic dynamics at higher temperatures that resemble static disorder at fast shutter speeds, with correlated ferroelectric fluctuations along the <100> c direction. We show that this anisotropy naturally emerges from a Ginzburg–Landau model that couples polarization fluctuations through long-range elastic interactions. By accessing time-dependent atomic correlations in energy materials, we resolve the long-standing disagreement between local and average structure probes and show that spontaneous anisotropy is ubiquitous in cubic IV–VI materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Designing Glass and Crystalline Phases of Metal–Bis(acetamide) Networks to Promote High Optical Contrast

Owing to their high tunability and predictable structures, metal–organic materials offer a powerful platform to study glass formation and crystallization processes and to design glasses with unique properties. In this work, we report a novel series of glass-forming metal–ethylenebis(acetamide) networks that undergo reversible glass and crystallization transitions below 200 °C. The glass-transition temperatures, crystallization kinetics, and glass stability of these materials are readily tunable, either by synthetic modification or by liquid-phase blending, to form binary glasses. Pair distribution function (PDF) analysis reveals extended structural correlations in both single and binary metal–bis(acetamide) glasses and highlights the important role of metal–metal correlations during structural evolution across glass–crystal transitions. Notably, the glass and crystalline phases of a Co–ethylenebis(acetamide) binary network feature a large reflectivity contrast ratio of 4.8 that results from changes in the local coordination environment around Co centers. These results provide new insights into glass–crystal transitions in metal–organic materials and have exciting implications for optical switching, rewritable data storage, and functional glass ceramics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

diffpy.mpdf : open-source software for magnetic pair distribution function analysis

The open-source Python package diffpy.mpdf , part of the DiffPy suite for diffraction and pair distribution function analysis, provides a user-friendly approach for performing magnetic pair distribution function (mPDF) analysis. The package builds on existing libraries in the DiffPy suite to allow users to create models of magnetic structures and calculate corresponding one- and three-dimensional mPDF patterns. diffpy.mpdf can be used to perform fits to mPDF data either in isolation or in combination with atomic pair distribution function data for joint refinement of the atomic and magnetic structure. Examples are given using MnO and MnTe as representative antiferromagnetic compounds and MnSb as a representative ferromagnet.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Liquid and Glass Phases of an Alkylguanidinium Sulfonate Hydrogen-Bonded Organic Framework

Glassy phases of framework materials feature unique and tunable properties that are advantageous for gas separation membranes, solid electrolytes, and phase-change memory applications. However, the structural and chemical diversity of porous frameworks that can be liquified and quenched into a glass has been limited by thermal decomposition at-or below-the high temperatures required to induce a melting transition. Utilizing a desymmetrization strategy, in this work we report a new guanidinium organosulfonate hydrogen-bonded organic framework (HOF) that melts and vitrifies below 100 °C. In this low-temperature regime, non-covalent interactions between guest molecules and the porous framework become a dominant contributor to the overall stability of the structure, resulting in unusual phase behavior such as guest dependent melting, glass, and recrystallization transitions. Through molecular dynamics simulations and pair distribution function analysis, we show that the local structure of the amorphous liquid and glass phase resembles that of the parent crystalline framework. Access to molten phases of framework materials at moderate temperatures should permit the use of more thermally sensitive functional groups and enhance the structural control and tunability that can be realized in network-forming glasses.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗