Design of crystal-like aperiodic solids with selective disorder--phonon coupling

Functional materials design normally focuses on structurally-ordered systems because disorder is considered detrimental to many important physical properties. Here we challenge this paradigm by showing that particular types of strongly-correlated disorder can give rise to useful characteristics that are inaccessible to ordered states. A judicious combination of low-symmetry building unit and high-symmetry topological template leads to aperiodic "procrystalline" solids that harbour this type of topological disorder. We identify key classes of procrystalline states together with their characteristic diffraction behaviour, and establish a variety of mappings onto known and target materials. Crucially, the strongly-correlated disorder we consider is associated with specific sets of modulation periodicities distributed throughout the Brillouin zone. Lattice dynamical calculations reveal selective disorder-phonon coupling to lattice vibrations characterised by these same periodicities. The principal effect on the phonon spectrum is to bring about dispersion in energy rather than wave-vector, as in the poorly-understood "waterfall" effect observed in relaxor ferroelectrics. This property of procrystalline solids suggests a mechanism by which strongly-correlated topological disorder might allow new and useful functionalities, including independently-optimised thermal and electronic transport behaviour as required for high-performance thermoelectrics.Comment: 4 figure

Defect-dependent colossal negative thermal expansion in UiO-66(Hf) metal-organic framework

Thermally-densified hafnium terephthalate UiO-66(Hf) is shown to exhibit the strongest isotropic negative thermal expansion (NTE) effect yet reported for a metal-organic framework (MOF). Incorporation of correlated vacancy defects within the framework affects both the extent of thermal densification and the magnitude of NTE observed in the densified product. We thus demonstrate that defect inclusion can be used to tune systematically the physical behaviour of a MOF.Comment: 8 pages, 4 figures, revise

PASCal Python: A Principal Axis Strain Calculator

The response of crystalline materials to external stimuli: whether temperature, pressure or electrochemical potential, is critical for both our understanding of materials and their use. This information can be readily obtained through in-situ diffraction experiments, however if the intrinsic anisotropy of crystals is not taken into account, the true behaviour of crystals can be overlooked. This is particularly true for anomalous mechanical properties of great topical interest, such as negative linear or area compressibility (Cairns & Goodwin, 2015; Hodgson et al., 2014), negative thermal expansion (Chen et al., 2015) or strongly anisotropic electrochemical strain (Kondrakov et al., 2017). We have developed PASCal, Principal Axis Strain Calculator, a widely used web tool that implements the rapid calculation of principal strains and fitting to many common models for equations of state. It provides a simple web form user interface designed to be able to be used by all levels of experience. This new version of PASCal is written in Python using the standard scientific Python stack (Harris et al., 2020; Virtanen et al., 2020), is released open source under the MIT license, and significantly extends the feature set of the original closed-source Fortran, Perl and Gnuplot webtool (Cliffe & Goodwin, 2012). Significant additional attention has been paid to testing, documentation, modularisation and reproducibility, enabling the main app functionality to now also be accessed directly through a Python API. The web app is deployed online at https://www.pascalapp.co.uk with the associated source code and documentation available on GitHub at MJCliffe/PASCal

SquidLab—A user-friendly program for background subtraction and fitting of magnetization data

We present an open-source program free to download for academic use with a full user-friendly graphical interface for performing flexible and robust background subtraction and dipole fitting on magnetization data. For magnetic samples with small moment sizes or sample environments with large or asymmetric magnetic backgrounds, it can become necessary to separate background and sample contributions to each measured raw voltage measurement before fitting the dipole signal to extract magnetic moments. Originally designed for use with pressure cells on a Quantum Design MPMS3 SQUID magnetometer, SquidLab is a modular object-oriented platform implemented in Matlab with a range of importers for different widely available magnetometer systems (including MPMS, MPMS-XL, MPMS-IQuantum, MPMS3, and S700X models) and has been tested with a broad variety of background and signal types. The software allows background subtraction of baseline signals, signal preprocessing, and performing fits to dipole data using Levenberg–Marquardt non-linear least squares or a singular value decomposition linear algebra algorithm that excels at picking out noisy or weak dipole signals. A plugin system allows users to easily extend the built-in functionality with their own importers, processes, or fitting algorithms. SquidLab can be downloaded, under Academic License, from the University of Warwick depository (wrap.warwick.ac.uk/129665)

Magnetic order in a metal thiocyanate perovskite-analogue

Metal thiocyanate perovskite-analogues are a growing class of materials, but although they contain paramagnetic cations there have been no reports of their magnetic properties. Due to the large separations between the paramagnetic cations, with a shortest through-bond distance of 15.1 Å, these materials might be expected to be good examples of paramagnets. In this communication we investigate the magnetic properties of a metal thiocyanate framework Cr[Bi(SCN)6]·xH2O. We find that Cr[Bi(SCN)6]·xH2O undergoes long-range magnetic order at TN = 4.0(2) K. We use neutron powder diffraction to determine that Cr[Bi(SCN)6]·xH2O has a MnO-type {111}cubic-ordering as its ground state, consistent with frustrated nearest- and next-nearest-neighbour antiferromagnetic interactions. This suggests that appropriate design of metal thiocyanate perovskite-analogue structures may reveal a rich vein of frustrated magnetism

Short-range ordering in a battery electrode, the 'cation-disordered' rocksalt Li1.25Nb0.25Mn0.5O2.

Cation order, with a local structure related to γ-LiFeO2, is observed in the nominally cation-disordered Li-excess rocksalt Li1.25Nb0.25Mn0.5O2via X-ray diffraction, neutron pair distribution function analysis, magnetic susceptibility and NMR spectroscopy. The correlation length of ordering depends on synthesis conditions and has implications for the electrochemistry of these phases.EPSRC: EP/L015978/1 Basic Energy Science, US Department of Energy: DE-SC001258

Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials

Direct imaging of correlated defect nanodomains in a metal-organic framework

Defect engineering can enhance key properties of metal-organic frameworks (MOFs). Tailoring the distribution of defects, for example in correlated nanodomains, requires characterization across length scales. However, a critical nanoscale characterization gap has emerged between the bulk diffraction techniques used to detect defect nanodomains and the sub-nanometer imaging used to observe individual defects. Here, we demonstrate that the emerging technique of scanning electron diffraction (SED) can bridge this gap uniquely enabling both nanoscale crystallographic analysis and the lowdose formation of multiple diffraction contrast images for defect analysis in MOFs. We directly image defect nanodomains in the MOF UiO-66(Hf) over an area of ca. 1 000 nm and with a spatial resolution ca. 5 nm to reveal domain morphology and distribution. Based on these observations, we suggest possible crystal growth processes underpinning synthetic control of defect nanodomains. We also identify likely dislocations and small angle grain boundaries, illustrating that SED could be a key technique in developing the potential for engineering the distribution of defects, or “microstructure”, in functional MOF design