Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is their computational scaling $O(n^3 p^3)$, which is cubic in the number of both inputs $n$ (e.g., time points or locations) and outputs $p$. For this reason, a popular class of MOGPs assumes that the data live around a low-dimensional linear subspace, reducing the complexity to $O(n^3 m^3)$. However, this cost is still cubic in the dimensionality of the subspace $m$, which is still prohibitively expensive for many applications. We propose the use of a sufficient statistic of the data to accelerate inference and learning in MOGPs with orthogonal bases. The method achieves linear scaling in $m$ in practice, allowing these models to scale to large $m$ without sacrificing significant expressivity or requiring approximation. This advance opens up a wide range of real-world tasks and can be combined with existing GP approximations in a plug-and-play way. We demonstrate the efficacy of the method on various synthetic and real-world data sets.

Materials for vehicular methane storage have been extensively studied, although no suitable material has been found. In this work, we use molecular simulation to investigate three types of carbon-based materials, Schwarzites, layered graphenes, and carbon nanoscrolls, for use in vehicular methane storage under adsorption conditions of 65 bar and 298 K and desorption conditions of 5.8 bar and 358 K. Ten different Schwarzites were tested and found to have high adsorption with maximums at 273 V-STP/V, but middling deliverable capacities of no more than 131 V-STP/V. Layered graphene and graphene nanoscrolls were found to have extremely high CH4 adsorption capacities of 355 and 339 V-STP/V, respectively, when the interlayer distance was optimized to 11 angstrom. The deliverable capacities of perfectly layered graphene and graphene nanoscrolls were also found to be exceptional with values of 266 and 252 V-STP/V, respectively, with optimized interlayer distances. These values make idealized graphene and nanoscrolls the record holders for adsorption and deliverable capacities under vehicular methane storage conditions.

A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments.

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.

Schwarzites are crystalline, 3D porous structures with a stable negative curvature formed of sp2-hybridized carbon atoms. These structures present topologies with tunable porous size and shape and unusual mechanical properties. In this work, we have investigated the mechanical behavior under compressive strain and energy absorption of four different Schwarzites. We considered two Schwarzites families, the so-called Gyroid and Primitive and two structures from each family. We carried out reactive molecular dynamics simulations, using the ReaxFF force field as available in the LAMMPS code. Our results also show they exhibit remarkable resilience under mechanical compression. They can be reduced to half of their original size before structural failure (fracture) occurs.

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis, implemented within the open-source, ab initio framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds characterized in the AFLOW.org repository, and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof of concept, thermodynamic characterizations have been performed for more than 1300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion-including 17 promising C15 b-type structures and 2 half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis and is located at aflow.org/aflow-chull .

Graphene nanowiggles (GNW) are graphene-based nanostructures obtained by making alternated regular cuts in pristine graphene nanoribbons. GNW were recently synthesized and it was demonstrated that they exhibit tunable electronic and magnetic properties by just varying their shape. Here, we have investigated the mechanical properties and fracture patterns of a large number of GNW of different shapes and sizes using fully atomistic reactive molecular dynamics simulations. Our results show that the GNW mechanical properties are strongly dependent on its shape and size and, as a general trend narrow sheets have larger ultimate strength and Young's modulus than wide ones. The estimated Young's modulus values were found to be in a range of ~ 100-1000 GPa and the ultimate strength in a range of ~ 20-110 GPa, depending on GNW shape. Also, super-ductile behaviour under strain was observed for some structures.

Carbon nanoscrolls are similar to multi-walled carbon nanotubes but constructed from rolled graphene sheets into papyrus-like structures. In this work, molecular simulations are used to evaluate the post-combustion CO2 capture properties of nanoscrolls made of graphene, alpha-, beta-, and gamma-graphyne, boron nitride, and three types of carbon nitride. The CO2 uptake capacity, CO2/N-2 selectivity and CO2 working capacity were computed with grand canonical Monte Carlo simulations at conditions relevant to post-combustion CO2 capture. The interlayer spacing of the nanoscrolls was optimized for each property and sheet material. For graphene nanoscrolls, the optimal interlayer spacing of 7.3 angstrom was identified for both the CO2 uptake and selectivity, while for working capacity the optimal interlayer spacing was determined to be 8.6 angstrom. It was found that the CO2 uptake capacity of the materials correlated to the density of the sheets from which they were formed. Nanoscrolls made from graphene and boron nitride, which have the highest number of atoms per unit area, also showed the highest CO2 uptakes. At 0.15 bar CO2, 313 K, graphene and boron nitride nanoscrolls exhibited exceptional CO2 uptake capacities of 7.7 and 8.2 mmol/ g, respectively, while also exhibiting high CO2/ N-2 selectivities of 135 and 153, respectively. Molecular dynamics simulations were used to examine the adsorption kinetics. The simulations showed that an empty graphene nanoscroll with a roll length of 200 angstrom could adsorb CO2 into the center of the roll within 10 ns. Materials with pores that can allow CO2 to pass through, such as graphynes, showed much faster adsorption times. (c) 2016 Elsevier Ltd. All rights reserved.

Metallic glasses attract considerable interest due to their unique combination of superb properties and processability. Predicting their formation from known alloy parameters remains the major hindrance to the discovery of new systems. Here, we propose a descriptor based on the heuristics that structural and energetic 'confusion' obstructs crystalline growth, and demonstrate its validity by experiments on two well-known glass-forming alloy systems. We then develop a robust model for predicting glass formation ability based on the geometrical and energetic features of crystalline phases calculated ab initio in the AFLOW framework. Our findings indicate that the formation of metallic glass phases could be much more common than currently thought, with more than 17% of binary alloy systems potential glass formers. Our approach pinpoints favourable compositions and demonstrates that smart descriptors, based solely on alloy properties available in online repositories, offer the sought-after key for accelerated discovery of metallic glasses.

Despite two decades of studies, the formation of metallic glasses, very promising systems for industrial applications, still remains mostly unexplained. This lack of knowledge hinders the search for new systems, still performed with combinatorial trial and error. In the past, it was speculated that some sort of "confusion" during crystallization could play a key role during their formation. In this article, we propose a heuristic descriptor quantifying such confusion. It is based on the "thermodynamic density of competing crystalline states", parameterized from high-throughput ab-initio calculations. The existence of highly enthalpy-degenerate but geometrically-different phases frustrates the crystallization process and promotes glass formation. Two test beds are considered. A good and a bad glass-former, CuZr and NiZr, are experimentally characterized with high-throughput synthesis. The experimental results corroborate the capability of the heuristic descriptor in predicting glass forming ability through the compositional space. Our analysis is expected to deepen the understanding of the underlying mechanisms and to accelerate the discovery of novel metallic glasses.

We have investigated the crystallization kinetics of Cu50Zr50 metallic glass thin films using nanocalorimetry. The crystallization process is growth-controlled during heating and nucleation-controlled during cooling, resulting in different critical heating and cooling rates to suppress crystallization. Measurements over a wide range of scanning rates (13 K/s to 21,000 K/s) reveal that crystallization does not follow Arrhenius kinetics upon heating. Instead, the behavior on heating is well described by a fragility based model of growth-controlled kinetics that takes into account breakdown of the Stokes-Einstein relationship. Upon cooling, the quench rate required to suppress crystallization of the melt is much higher than for bulk samples. This reduced asymmetry in critical heating and cooling rates compared to bulk materials suggests that crystallization of the thin-film metallic glass is controlled by heterogeneous nucleation. (C) 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Carbon nitride-based nanostructures have attracted special attention (from theory and experiments) due to their remarkable electromechanical properties. In this work we have investigated the mechanical properties of some graphene-like carbon nitride membranes through fully atomistic reactive molecular dynamics simulations. We have analyzed three different structures of these CN families, the so-called graphene-based g-CN, triazine-based g-C3N4 and heptazine-based g-C3N4. The stretching dynamics of these membranes was studied for deformations along their two main axes and at three different temperatures: 10K, 300K and 600K. We show that g-CN membranes have the lowest ultimate fracture strain value, followed by heptazine-based and triazine-based ones, respectively. This behavior can be explained in terms of their differences in terms of density values, topologies and types of chemical bonds. The dependency of the fracture patterns on the stretching directions is also discussed.

Graphene, in single layer or multi-layer forms, holds great promise for future electronics and high-temperature applications. Resistance to oxidation, an important property for high-temperature applications, has not yet been extensively investigated. Controlled thinning of multi-layer graphene (MLG), e.g., by plasma or laser processing is another challenge, since the existing methods produce non-uniform thinning or introduce undesirable defects in the basal plane. We report here that heating to extremely high temperatures (exceeding 2000 K) and controllable layer-by-layer burning (thinning) can be achieved by low-power laser processing of suspended high-quality MLG in air in “cold-wall” reactor configuration. In contrast, localized laser heating of supported samples results in non-uniform graphene burning at much higher rates. Fully atomistic molecular dynamics simulations were also performed to reveal details of oxidation mechanisms leading to uniform layer-by-layer graphene gasification. The extraordinary resistance of MLG to oxidation paves the way to novel high-temperature applications as continuum light source or scaffolding material.

Nanoscrolls consist of sheets rolled up into a papyrus-like form. Their open ends produce great radial flexibility, which can be exploited for a large variety of applications, from actuators to hydrogen storage. They have been successfully synthesized from different materials, including carbon and boron nitride. In this work we have investigated, through fully atomistic molecular dynamics simulations, the dynamics of scroll formation for a series of graphene-like carbon nitride (CN) two-dimensional systems: g-CN, triazine-based (g-C3N4), and heptazine-based (g-C3N4). Carbon nitride (CN) structures have been attracting great attention since their prediction as super hard materials. Recently, graphene-like carbon nitride (g-CN) structures have been synthesized with distinct stoichiometry and morphologies. By combining these unique CN characteristics with the structural properties inherent to nanoscrolls new nanostructures with very attractive mechanical and electronic properties could be formed. Our results show that stable nanoscrolls can be formed for all of CN structures we have investigated here. As the CN sheets have been already synthesized, these new scrolled structures are perfectly feasible and within our present-day technology.

The growth and self-assembling properties of nickel-tetraphenyl porphyrins (NiTPP) on the Cu(111) surface are analysed via scanning tunnelling microscopy (STM), X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT). For low coverage, STM results show that NiTPP molecules diffuse on the terrace until they reach the step edge of the copper surface forming a 1D system with disordered orientation along the step edges. The nucleation process into a 2D superstructure was observed to occur via the interaction of molecules attached to the already nucleated 1D structure, reorienting molecules. For monolayer range coverage a 2D nearly squared self-assembled array with the emergence of chiral domains was observed. The XPS results of the Ni 2p(3/2) core levels exhibit a 2.6 eV chemical shift between the mono- and multilayer configuration of NiTPP. DFT calculations show that the observed chemical shifts of Ni 2p(3/2) occur due to the interaction of 3d orbitals of Ni with the Cu(111) substrate.

Recently, it was proposed based on classical elasticity theory and experiments at macroscale, that the conformations of sheets inside cylindrical tubes present a universal behavior. A natural question is whether this behavior still holds at nanoscale. Based on molecular-dynamics simulations and analytical modeling for graphene and boron nitride membranes confined inside carbon nanotubes, we show that the class of universality observed at macroscale is violated at nanoscale. The precise origin of these discrepancies is addressed and proven to be related to both surface and atomistic effects.

Nanoscrolls are papyrus-like nanostructures which present unique properties due to their open ended morphology. These properties can be exploited in a plethora of technological applications, leading to the design of novel and interesting devices. During the past decade, significant advances in the synthesis and characterization of these structures have been made, but many challenges still remain. In this mini review we provide an overview on their history, experimental synthesis methods, basic properties and application perspectives.

In this work we report new silicon and germanium tubular nanostructures with no corresponding stable carbon analogues. The electronic and mechanical properties of these new tubes were investigated through ab initio methods. Our results show that these structures have lower energy than their corresponding nanoribbon structures and are stable up to high temperatures (500 and 1000 K, for silicon and germanium tubes, respectively). Both tubes are semiconducting with small indirect band gaps, which can be significantly altered by both compressive and tensile strains. Large bandgap variations of almost 50% were observed for strain rates as small as 3%, suggesting their possible applications in sensor devices. They also present high Young's modulus values (0.25 and 0.15 TPa, respectively). TEM images were simulated to help in the identification of these new structures.