Combined electronic structure and evolutionary search approach to materials design

Influence of Alkali Metal Cations on the Oxygen Reduction Activity of Pt5Y and Pt5Gd Alloys

Electrolyte species can significantly influence the electrocatalytic performance. In this work, we investigate the impact of alkali metal cations on the oxygen reduction reaction (ORR) on active Pt5Gd and Pt5Y polycrystalline electrodes. Due to the strain effects, Pt alloys exhibit a higher kinetic current density of ORR than pure Pt electrodes in acidic media. In alkaline solutions, the kinetic current density of ORR for Pt alloys decreases linearly with the decreasing hydration energy in the order of Li+ > Na+ > K+ > Rb+ > Cs+, whereas Pt shows the opposite trend. To gain further insights into these experimental results, we conduct complementary density functional theory calculations considering the effects of both electrode surface strain and electrolyte chemistry. The computational results reveal that the different trends in the ORR activity in alkaline media can be explained by the change in the adsorption energy of reaction intermediates with applied surface strain in the presence of alkali metal cations. Our findings provide important insights into the effects of the electrolyte and the strain conditions on the electrocatalytic performance and thus offer valuable guidelines for optimizing Pt-based electrocatalysts.

Low-Electronegativity Mn-Contraction of PtMn Nanodendrites Boosts Oxygen Reduction Durability

Platinum metal (PtM, M=Ni, Fe, Co) alloys catalysts show high oxygen reduction reaction (ORR) activity due to their well-known strain and ligand effects. However, these PtM alloys usually suffer from a deficient ORR durability in acidic environment as the alloyed metal is prone to be dissolved due to its high electronegativity. Herein, we report a new class of PtMn alloy nanodendrite catalyst with low-electronegativity Mn-contraction for boosting the oxygen reduction durability of fuel cells. The moderate strain in PtMn, induced by Mn contraction, yields optimal oxygen reduction activity at 0.53 A mg-1 at 0.9 V versus reversible hydrogen electrode (RHE). Most importantly, we show that relative to well-known high-electronegativity Ni-based Pt alloy counterpart, the PtMn nanodendrite catalyst experiences less transition metals' dissolution in acidic solution and achieves an outstanding mass activity retention of 96 % after 10,000 degradation cycles. Density functional theory calculation reveals that PtMn alloys are thermodynamically more stable than PtNi alloys in terms of formation enthalpy and cohesive energy. The PtMn nanodendrite-based membrane electrode assembly delivers an outstanding peak power density of 1.36 W cm-2 at a low Pt loading and high-performance retention over 50 h operations at 0.6 V in H2 -O2 hydrogen fuel cells.

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.

Motor imagery classification using sparse representations: an exploratory study

The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of [Formula: see text] and [Formula: see text] respectively while the MLP is [Formula: see text], representing a gain between [Formula: see text] and [Formula: see text]. The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of [Formula: see text] among the subjects in the base, while other model reach [Formula: see text]. The improvement of self-adaptive mechanisms that respond efficiently to the user's context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

Purpose

A new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters.

Method

This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features.

Results

We tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159% ± 0.693, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010, and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed.

Conclusion

This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.

Ab Initio Machine Learning in Chemical Compound Space

Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.

Electronic-structure methods for materials design

The accuracy and efficiency of electronic-structure methods to understand, predict and design the properties of materials has driven a new paradigm in research. Simulations can greatly accelerate the identification, characterization and optimization of materials, with this acceleration driven by continuous progress in theory, algorithms and hardware, and by adaptation of concepts and tools from computer science. Nevertheless, the capability to identify and characterize materials relies on the predictive accuracy of the underlying physical descriptions, and on the ability to capture the complexity of realistic systems. We provide here an overview of electronic-structure methods, of their application to the prediction of materials properties, and of the different strategies employed towards the broader goals of materials design and discovery.

Rare-Earth Incorporated Alloy Catalysts: Synthesis, Properties, and Applications

To improve the performance of metallic catalysts, alloying provides an efficient methodology to design state-of-the-art materials. As emerging functional materials, rare-earth metal compounds can integrate the unique orbital structure and catalytic behavior of rare earth elements into metallic materials. Such rare-earth containing alloy catalysts proffer an opportunity to tailor electronic properties, tune charged carrier transport, and synergize surface reactivity, which are expected to significantly improve the performance and stability of catalysis. Despite its significance, there are only few reviews on rare earth containing alloys or related topics. This review summarizes the composition, synthesis, and applications of rare earth containing alloys in the field of catalysis. Subsequent to comprehensively summarizing and constructively discussing the existing work, the challenges and possibilities of future research on rare-earth metal compound materials are evaluated.

Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor

A molecular crystal structure prediction workflow, based on the random structure generator, Genarris, and the genetic algorithm (GA), GAtor, is successfully applied to two energetic materials and a chiral arene.

LAN: A Materials Notation for Two-Dimensional Layered Assemblies

Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation, and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not yet fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with a number of example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture, and transfer of materials knowledge in the area of 2D layered materials.

Exploring chemical compound space with quantum-based machine learning

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space - the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

Atom Classification Model for Total Energy Evaluation of Two-Dimensional Multicomponent Materials

The tunable properties of materials originate from variety of structures; however, it is still a challenge to give an accurate and fast evaluation of stabilities for screening numerous candidates. Herein, we propose an atom classification model to describe the multicomponent materials based on the structural recognition, in which the atoms are classified to estimate the total energies. Taking two-dimensional planar C_1-xB_x and C_1-2x(BN)_x as examples, we have found that the test error of total energies is about 3 meV per atom. Notably, the distributions of classified atoms demonstrate the evolution of configurations as a function of temperature, providing a clearer picture of phase transition. In addition, our method is universal, which can be flexibly extended to the bulk structures with more components.

Data-Driven Materials Science: Status, Challenges, and Perspectives

Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

Acceleration of Inverse Molecular Design by Using Predictive Techniques

This study addresses one of the most important drawbacks inherently related to molecular searches in chemical compound space by greedy algorithms such as Best First Search and Genetic Algorithm, i.e., the large computational cost required to optimize one or more quantum-chemical properties. Significant speed-ups are obtained by initial property screening via predictive techniques starting already from very small databases. It is shown that the attainable acceleration depends heavily on the molecular properties, the predictive model, the molecular descriptor, and the current size of the database. We discuss the implementation and performance of predictive techniques in molecular searches based on a fixed molecular framework with a selection of sites to be filled with groups from a chemical fragment library. It is shown that for some properties speed-ups of a factor of 5 to even 20 can be obtained, while inverse design procedures on more complex properties still reach speed-ups of a factor of 2 without losing performance.

Early Years of High-Throughput Experimentation and Combinatorial Approaches in Catalysis and Materials Science

This is a report on the early years of combinatorial materials science and technology. High-throughput technologies (HTTs) are found in life- and materials-science laboratories. Although HTTs have long been the standard in life sciences in academia as well as in industry, HTTs in materials science have become the standard in industry but not in academia. In life science, successful drugs developed with HTTs have been reported, but there is no information on successful materials developed with HTTs that have made it to the market. Some initial development of HTTs in materials science is summarized, especially early applications of artificial intelligence. This outlook attempts to summarize the development of combinatorial materials sciences from the early years to today.

Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning

Growing interest in the potential applications of two-dimensional (2D) materials has fueled advancement in the identification of 2D systems with exotic properties. Increasingly, the bottleneck in this field is the synthesis of these materials. Although theoretical calculations have predicted a myriad of promising 2D materials, only a few dozen have been experimentally realized since the initial discovery of graphene. Here, we adapt the state-of-the-art positive and unlabeled (PU) machine learning framework to predict which theoretically proposed 2D materials have the highest likelihood of being successfully synthesized. Using elemental information and data from high-throughput density functional theory calculations, we apply the PU learning method to the MXene family of 2D transition metal carbides, carbonitrides, and nitrides, and their layered precursor MAX phases, and identify 18 MXene compounds that are highly promising candidates for synthesis. By considering both the MXenes and their precursors, we further propose 20 synthesizable MAX phases that can be chemically exfoliated to produce MXenes.

Exploring chemical space with alchemical derivatives: alchemical transformations of H through Ar and their ions as a proof of concept

Alchemical derivatives have been used previously to obtain information about transformations in which the number of electrons is unchanged. Here an approach for combining changes in both the number of electrons and the nuclear charge is presented.

L12 Atomic Ordered Substrate Enhanced Pt-Skin Cu3Pt Catalyst for Efficient Oxygen Reduction Reaction

Constructing Pt skin on intermetallics has been confirmed as an efficient strategy to boost oxygen reduction reaction (ORR) kinetics. However, there still lacks a systematic study on revealing the influence of low-Pt-content intermetallic substrates (L1₂-PtM₃). In this paper, Pt skin-encapsulated low-Pt-mole-fraction L1₂ Cu₃Pt has been constructed (denoted as Pt-o-Cu₃Pt/C) and compared with its disordered analogue (denoted as Pt-d-Cu₃Pt/C). The L1₂ substrate shows a contracted lattice structure and provides Pt-o-Cu₃Pt/C with an excellent specific activity of 1.73 mA cm^-2, which is 1.4- and 8.4-fold higher than that of Pt-d-Cu₃Pt/C and commercial Pt/C, respectively. Density functional theory calculations reveal that this superior performance is attributed to the more favorable oxygen adsorption energy of surface Pt atoms. Furthermore, the lower formation energy of L1₂ Cu₃Pt combined with the enhanced antioxygenation of Pt provide Pt-o-Cu₃Pt/C with a superior durability, showing only a 12.5% loss in mass activity after 5000 potential cycles. Therefore, it is suggested that L1₂ atomic ordered structure with a low Pt fraction is a promising substrate for building high-performance Pt-skin catalysts for ORR.

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction

We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a Z' = 2 structure with P1̅ symmetry and a scaffold packing motif, which has not been reported previously.

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozen 2D materials have been successfully synthesized or exfoliated. Here, we search for 2D materials that can be easily exfoliated from their parent compounds. Starting from 108,423 unique, experimentally known 3D compounds, we identify a subset of 5,619 compounds that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van der Waals density functional theory, validated against experimental structural data and calculated random phase approximation binding energies, further allowed the identification of 1,825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1,036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds, we explore vibrational, electronic, magnetic and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors.

Exploring Chemical Space with Alchemical Derivatives: BN-Simultaneous Substitution Patterns in C60

With the idea of using alchemical derivatives to explore in an efficient, computer- and cost-effective way Chemical Space was launched several years ago. In the context of Conceptual DFT response functions, these energies vs nuclear charge derivatives permit the estimatation of the energy of transmutants of a given starting or reference molecule showing different nuclear compositions. After an explorative study on small and planar molecules ( Balawender et al. J. Chem. Theory Comput. 2013 , 9 , 5327 ) by the present authors of this paper, the present study fully exploits the computational advantages of the alchemical derivatives in larger three-dimensional systems. Starting from a single reference calculation on C₆₀, the complete BN substitution pattern, from single substituted C₅₈BN via the belt (C₂₀(BN)₂₀ and the ball C₁₂(BN)₂₄ structures to the fully substituted (BN)₃₀, is explored. Successive and simultaneous substitution strategies are followed and compared, indicating that both techniques yield identical results up to 13 substitutions but that for higher substitutions the simultaneous approach needs to be taken. Due to the cost-efficiency of the algorithm this path can indeed be followed as opposed to earlier work in the literature where for each step a full SCF calculation was at stake leading to prohibitively large computational demands for adopting the simultaneous approach. Previously formulated rules governing the substitution pattern by Kar and co-workers are scrutinized in this context and reformulated giving chemical insight in the gradual substitution process and the relative energies of the isomers. In its present form the method offers an interesting venue to study BN substitution patterns in higher fullerenes and graphene and in general paves the way for more efficient exploration of the Chemical Space.

Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction

The effects of evolutionary niching are investigated for the crystal structure prediction of 1,3-dibromo-2-chloro-5-fluorobenzene.

The atomic simulation environment-a Python library for working with atoms

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Tuning the HOMO-LUMO Energy Gap of Small Diamondoids Using Inverse Molecular Design

Functionalized diamondoids show great potential as building blocks for various new optoelectronic applications. However, until now, only simple mono and double substitutions were investigated. In this work, we considered up to 10 and 6 sites for functionalization of the two smallest diamondoids, adamantane and diamantane, respectively, in search for diamondoid derivatives with a minimal and maximal HOMO-LUMO energy gap. To this end, the energy gap was optimized systematically using an inverse molecular design methodology based on the best-first search algorithm combined with a Monte Carlo component to escape local optima. Adamantane derivatives were found with HOMO-LUMO gaps ranging from 2.42 to 10.63 eV, with 9.45 eV being the energy gap of pure adamantane. For diamantane, similar values were obtained. The structures with the lowest HOMO-LUMO gaps showed apparent push-pull character. The push character is mainly formed by sulfur or nitrogen dopants and thiol groups, whereas the pull character is predominantly determined by the presence of electron-withdrawing nitro or carbonyl groups assisted by amino and hydroxyl groups via the formation of intramolecular hydrogen bonds. In contrast, maximal HOMO-LUMO gaps were obtained by introducing numerous electronegative groups.

Fast and accurate predictions of covalent bonds in chemical space

We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSiP, HSiAs, HGeN, HGeP, HGeAs); and (v) H2 (+) single bond with 1 electron.

CO oxidation on Rh-doped hexadecagold clusters

Exploring the unique catalytic properties of gold clusters associated with specific nano-architectures is essential for designing improved catalysts with a high mass-specific activity.

Scalable Nanoporous (Pt1-xNix)3Al Intermetallic Compounds as Highly Active and Stable Catalysts for Oxygen Electroreduction

Author: Bimetallic platinum-nickel (Pt-Ni) alloys as oxygen reduction reaction (ORR) electrocatalysts show genuine potential to boost widespread use of low-temperature fuel cells in vehicles by virtue of their high catalytic activity. However, their practical implementation encounters primary challenges in structural and catalytic durability caused by the low formation heat of Pt-Ni alloys. Here, we report nanoporous (NP) (Pt_1-xNi_x)₃Al intermetallic nanoparticles as oxygen electroreduction catalyst NP (Pt_1-xNi_x)₃Al, which circumvents this problem by making use of the extraordinarily negative formation heats of Pt-Al and Ni-Al bonds. The NP (Pt_1-xNi_x)₃Al nanocatalyst, which is mass-produced by alloying/dealloying and mechanical crushing technologies, exhibits specific activity of 3.6 mA cm^-2_Pt and mass activity of 2.4 A mg^-1_Pt at 0.90 V as a result of both ligand and compressive strain effects, while strong Ni-Al and Pt-Al bonds ensure their exceptional durability by alleviating evolution of Pt, Ni, and Al components and dissolutions of Ni and Al atoms.

Unraveling the Planar-Globular Transition in Gold Nanoclusters through Evolutionary Search

Au nanoclusters are of technological relevance for catalysis, photonics, sensors, and of fundamental scientific interest owing to planar to globular structural transformation at an anomalously high number of atoms i.e. in the range 12–14. The nature and causes of this transition remain a mystery. In order to unravel this conundrum, high throughput density functional theory (DFT) calculations, coupled with a global structural optimization scheme based on a modified genetic algorithm (GA) are conducted. More than 20,000 Au₁₂, Au₁₃, and Au₁₄ nanoclusters are evaluated. With any DFT functional, globular and planar structures coexist across the size range of interest. The planar-globular transition is gradual at room temperature rather than a sharp transition as previously believed. The effects of anionicity, s-d band hybridization and long range interactions on the dimensional transition are quantified by using the structures adjacent to the minima. Anionicity marginally changes the relative stability of the clusters. The degree of s-d hybridization is varied via changing the Hubbard U value which corroborate that s-d hybridization alone does not stabilize planar structures. van der Waals interactions, on the other hand, stabilize globular structures. These results elucidate the balance between the different reasons of the dimensional transition in gold nanoclusters.

First principles search for n-type oxide, nitride, and sulfide thermoelectrics

Oxides have many potentially desirable characteristics for thermoelectric applications, including low cost and stability at high temperatures, but thus far there are few known high zT n-type oxide thermoelectrics. In this work, we use high-throughput first principles calculations to screen transition metal oxides, nitrides, and sulfides for candidate materials with high power factors and low thermal conductivity. We find a variety of promising materials, and we investigate these materials in detail in order to understand the mechanisms that cause them to have high power factors. These materials all combine a high density of states near the Fermi level with dispersive bands, reducing the trade-off between the Seebeck coefficient and the electrical conductivity, but they do so for several different reasons. In addition, our calculations indicate that many of our candidate materials have low thermal conductivity.

Efficient Ab initio Modeling of Random Multicomponent Alloys

We present in this Letter a novel small set of ordered structures (SSOS) method that allows extremely efficient ab initio modeling of random multicomponent alloys. Using inverse II-III spinel oxides and equiatomic quinary bcc (so-called high entropy) alloys as examples, we demonstrate that a SSOS can achieve the same accuracy as a large supercell or a well-converged cluster expansion, but with significantly reduced computational cost. In particular, because of this efficiency, a large number of quinary alloy compositions can be quickly screened, leading to the identification of several new possible high-entropy alloy chemistries. The SSOS method developed here can be broadly useful for the rapid computational design of multicomponent materials, especially those with a large number of alloying elements, a challenging problem for other approaches.

Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup

BACKGROUND/AIM

The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients.

METHODS

The participants were recruited from 6 Nordic memory clinics: 372 unselected patients [mean age 71.7 years (SD 8.6), 54% women] and 146 healthy elderly individuals [mean age 66.5 years (SD 7.7), 60% women]. After a standardized and comprehensive assessment, clinical diagnoses were made according to internationally accepted criteria by at least 2 clinicians. EEGs were recorded in a standardized way and analyzed independently of the clinical diagnoses, using the SPR method.

RESULTS

In receiver operating characteristic curve analyses, the qEEGs separated AD patients from healthy elderly individuals with an area under the curve (AUC) of 0.90, representing a sensitivity of 84% and a specificity of 81%. The qEEGs further separated patients with Lewy body dementia or Parkinson's disease dementia from AD patients with an AUC of 0.9, a sensitivity of 85% and a specificity of 87%.

CONCLUSION

qEEG using the SPR method could be a useful tool in dementia diagnostic workup.

Correlation between diffusion barriers and alloying energy in binary alloys

In this paper, we explore the notion that a negative alloying energy may act as a descriptor for long term stability of Pt-alloys as cathode catalysts in low temperature fuel cells.

Alchemical Variations of Intermolecular Energies According to Molecular Grand-Canonical Ensemble Density Functional Theory

Molecular grand-canonical density functional theory [J. Chem. Phys. 2006, 125, 154104] is employed for the alchemical variation of intermolecular energies due to changes in the chemical composition of small molecules. We investigate the interaction of a fixed binding target, formic acid, with a restricted chemical space, corresponding to an isoelectronic 10-proton system which includes molecules such as CH4, NH3, H2O, and HF. Differential expressions involving the nuclear chemical potential are derived, numerically evaluated, tested with respect to finite difference results, and discussed regarding their suitability as gradients of the intermolecular energy with respect to compositional variations.

Electronic structure of CoPt based systems: from bulk to nanoalloys

An accurate description of the local electronic structure is necessary for guiding the design of materials with targeted properties in a controlled way. For complex materials like nanoalloys, self-consistent tight-binding calculations should be a good alternative to ab initio methods, for handling the most complex and large systems (hundreds to thousands of atoms), provided that these parameterized method is well founded from ab initio ones that they intend to replace. Ab initio calculations (density functional theory) enabled us to derive rules for charge distribution as a function of structural change and alloying effects in Co and Pt based systems, from bulk to nanoalloys. A general local neutrality rule per site, orbital and species was found. Based on it, self-consistent tight-binding calculations could be implemented and applied to CoPt nanoalloys. A very good agreement is obtained between tight-binding and DFT calculations in terms of local electronic structure.

Advancing a distributed multi-scale computing framework for large-scale high-throughput discovery in materials science

We describe the development of a large-scale high-throughput application for discovery in materials science. Our point of departure is a computational framework for distributed multi-scale computation. We augment the original framework with a specialized module whose role is to route evaluation requests needed by the high-throughput application to a collection of available computational resources. We evaluate the feasibility and performance of the resulting high-throughput computational framework by carrying out a high-throughput study of battery solvents. Our results indicate that distributed multi-scale computing, by virtue of its adaptive nature, is particularly well-suited for building high-throughput applications.

Informatics derived materials databases for multifunctional properties

In this review, we provide an overview of the development of quantitative structure-property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure-property relationships; and even more importantly we are now in a position to build materials databases based on design 'intent' and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.

Electroencephalography as a clinical tool for diagnosing and monitoring attention deficit hyperactivity disorder: a cross-sectional study

OBJECTIVES

The aim of this study was to develop and test, for the first time, a multivariate diagnostic classifier of attention deficit hyperactivity disorder (ADHD) based on EEG coherence measures and chronological age.

SETTING

The participants were recruited in two specialised centres and three schools in Reykjavik.

PARTICIPANTS

The data are from a large cross-sectional cohort of 310 patients with ADHD and 351 controls, covering an age range from 5.8 to 14 years. ADHD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV) criteria using the K-SADS-PL semistructured interview. Participants in the control group were reported to be free of any mental or developmental disorders by their parents and had a score of less than 1.5 SDs above the age-appropriate norm on the ADHD Rating Scale-IV. Other than moderate or severe intellectual disability, no additional exclusion criteria were applied in order that the cohort reflected the typical cross section of patients with ADHD.

RESULTS

Diagnostic classifiers were developed using statistical pattern recognition for the entire age range and for specific age ranges and were tested using cross-validation and by application to a separate cohort of recordings not used in the development process. The age-specific classification approach was more accurate (76% accuracy in the independent test cohort; 81% cross-validation accuracy) than the age-independent version (76%; 73%). Chronological age was found to be an important classification feature.

CONCLUSIONS

The novel application of EEG-based classification methods presented here can offer significant benefit to the clinician by improving both the accuracy of initial diagnosis and ongoing monitoring of children and adolescents with ADHD. The most accurate possible diagnosis at a single point in time can be obtained by the age-specific classifiers, but the age-independent classifiers are also useful as they enable longitudinal monitoring of brain function.

Mass-selected nanoparticles of PtxY as model catalysts for oxygen electroreduction

Low-temperature fuel cells are limited by the oxygen reduction reaction, and their widespread implementation in automotive vehicles is hindered by the cost of platinum, currently the best-known catalyst for reducing oxygen in terms of both activity and stability. One solution is to decrease the amount of platinum required, for example by alloying, but without detrimentally affecting its properties. The alloy PtxY is known to be active and stable, but its synthesis in nanoparticulate form has proved challenging, which limits its further study. Herein we demonstrate the synthesis, characterization and catalyst testing of model PtxY nanoparticles prepared through the gas-aggregation technique. The catalysts reported here are highly active, with a mass activity of up to 3.05 A mgPt(-1) at 0.9 V versus a reversible hydrogen electrode. Using a variety of characterization techniques, we show that the enhanced activity of PtxY over elemental platinum results exclusively from a compressive strain exerted on the platinum surface atoms by the alloy core.

Al13@Pt42 core-shell cluster for oxygen reduction reaction

To increase Pt utilization for oxygen reduction reaction (ORR) in fuel cells, reducing particle sizes of Pt is a valid way. However, poisoning or surface oxidation limits the smallest size of Pt particles at 2.6 nm with a low utility of 20%. Here, using density functional theory calculations, we develop a core-shell Al₁₃@Pt₄₂ cluster as a catalyst for ORR. Benefit from alloying with Al in this cluster, the covalent Pt-Al bonding effectively activates the Pt atoms at the edge sites, enabling its high utility up to 70%. Valuably, the adsorption energy of O is located at the optimal range with 0.0–0.4 eV weaker than Pt(111), while OH-poisoning does not observed. Moreover, ORR comes from O₂ dissociation mechanism where the rate-limiting step is located at OH formation from O and H with a barrier of 0.59 eV, comparable with 0.50 eV of OH formation from O and H₂O on Pt(111).