Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born-Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code[1] was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.

As the challenges in continued scaling of the integrated circuit technology escalate every generation, there is an urgent need to find viable solutions for both the front-end-of-line (transistors) and the back-end-of-line (interconnects). For the interconnect technology, it is crucial to replace the conventional barrier and liner with much thinner alternatives so that the current driving capability of the interconnects can be maintained or even improved. Due to the inherent atomically thin body thicknesses, 2D materials have recently been proposed and explored as Cu diffusion barrier alternatives. In this Perspective article, a variety of 2D materials that have been studied, ranging from graphene, h-BN, MoS2, WSe2 to TaS2, will be reviewed. Their potentials will be evaluated based on several criteria, including fundamental material properties as well as the feasibility for technology integration. Using TaS2 as an example, we demonstrate a large set of promising properties and point out that there remain challenges in the integration aspects with a few possible solutions waiting for validation. Applications of 2D materials for other functions in Cu interconnects and for different metal types will also be introduced, including electromigration, cobalt interconnects, and radio-frequency transmission lines.

We have analysed structural motifs in the Deem database of hypothetical zeolites, to investigate whether the structural diversity found in this database can be well-represented by classical descriptors such as distances, angles, and ring sizes, or whether a more general representation of atomic structure, furnished by the smooth overlap of atomic positions (SOAP) method, is required to capture accurately structure-property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that SOAP with a cutoff-length of 6Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant inter-atomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors, and that the first three kernel principal components capture the main variability in the data set, allowing a 3D point cloud visualization of local environments in the Deem database. This "cloud atlas" of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine-learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework, and correlations between local volume and energy in a given framework.

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression and can be used conveniently to reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we provide a pedagogic overview of these data analysis schemes, including the use of the kernel trick to introduce an element of non-linearity while maintaining most of the convenience and the simplicity of linear approaches. We then introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.

The fracture-healing behavior of model physically associating triblock copolymer gels was investigated with experiments coupling shear rheometry and particle tracking flow visualization. Fractured gels were allowed to rest for specific time durations, and the extent of strength recovered during the resting time was quantified as a function of temperature (20−28°C) and gel concentration (5−6 vol %). Measured times for full strength recovery were an order of magnitude greater than characteristic relaxation times of the system. The Arrhenius activation energy for post-fracture strength recovery was found to be greater than the activation energy associated with stress relaxation, most likely due to the entropic barrier related to the healing mechanism of dangling chain reassociation with network junctions. S oft materials with well-defined mechanical properties are important in a variety of industrial and biomedical applications, including high toughness elastomers for seals and dampers, 1 hydrogels for synthetic cartilage, 2 hemostatic materials for wound dressing, 3 injectable materials for regenerative medicine, 4,5 and superabsorbent polymer hydrogels for applications as diverse as drug delivery to cement internal curing agents. 6 To exhibit optimum performance in these applications, the material's mechanical response to large applied deformations and their ability to heal following damage must be well understood. However, these nonlinear mechanical properties are difficult to characterize for soft materials using traditional experimental techniques. Standard tension and compression mechanical tests require self-supported samples and are thus not appropriate for materials that have fast relaxation times or contain large amounts of solvent.Recent work has shown shear rheometry to be an effective technique for characterizing the nonlinear deformation and fracture of soft materials. 7−10 To correlate the measured rheological response with the sample's macroscale behavior (e.g., formation of a fracture plane), rheophysical experiments are performed to simultaneously measure the local velocity profile during shear by employing a variety of techniques, including optical particle tracking, 11 ultrasonic velocimetry, 12 and NMR. 13 In this letter, we describe a rheophysical methodology for quantifying the fracture and self-healing behavior of a soft material. A temperature-dependent, physically associating polymer gel will be utilized as a model soft material. Shear rheometry coupled with an optical particle tracking system was used to directly observe the shear-induced formation and subsequent healing of the fracture plane within the material. Compared to the characteristic stress relaxation behavior, fracture-healing occurred over much greater time scales but with similar temperature dependence. Activation energy for healing was found to be greater than for relaxation, most likely due to the entropic barrier required for a chain to reassociate with a network junction.The model soft material is composed of triblock co...

Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.

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We report on the synthesis of cubic-phase garnet-type solid-state electrolytes based on Bi-doped Li7La3Zr2O12 (LLZO). Bi aliovalent substitution of Zr in LLZO utilizing the Pechini processing method is employed to synthesize Li7−xLa3Zr2−xBixO12 compounds. A strong dependence of the ionic conductivity on Bi content is observed, and under our synthesis and sintering conditions, a >100-fold increase over the un-doped sample is observed for x=0.75. Cubic-phase Li6La3Zr1BiO12 compounds are generated upon annealing in air in the temperature range 650 °C–900 °C. In contrast, in the absence of Bi, the cubic garnet phase of Li7La3Zr2O12 is not formed below 700 °C and a transformation to the tetragonal phase is observed at ∼900 °C for this un-doped compound. The role of Bi in lowering the formation temperature of the garnet cubic phase and in the ionic conductivity improvements is investigated in this work. We ascribe the effect of Bi-doping on ionic conductivity increments to changes in Li+-site occupancy and lattice parameters and the reduction in the formation temperature for the cubic-phase formation to rate enhancements of the solid-state reaction. To identify the site occupancy of Bi in the garnet structure, we employ synchrotron extended x-ray absorption fine structure spectroscopy. Our results indicate that Bi additions occupy the Zr-type sites exclusively, to within the accuracy of the measurements.