High-throughput Screening Of Materials Research Articles

Band structure diagram paths based on crystallography

Systematic and automatic calculations of the electronic band structure are a crucial component of computationally-driven high-throughput materials screening. An algorithm, for any crystal, to derive a unique description of the crystal structure together with a recommended band path is indispensable for this task. The electronic band structure is typically sampled along a path within the first Brillouin zone including the surface in reciprocal space. Some points in reciprocal space have higher site symmetries and/or have higher constraints than other points regarding the electronic band structure and therefore are likely to be more important than other points. This work categorizes points in reciprocal space according to their symmetry and provides recommended band paths that cover all special wavevector (k-vector) points and lines necessarily and sufficiently. Points in reciprocal space are labeled such that there is no conflict with the crystallographic convention. The k-vector coefficients of labeled points, which are located at Brillouin zone face and edge centers as well as vertices, are derived based on a primitive cell compatible with the crystallographic convention, including those with axial ratio-dependent coordinates. Furthermore, we provide an open-source implementation of the algorithms within our SeeK-path python code, to allow researchers to obtain k-vector coefficients and recommended band paths in an automated fashion. Finally, we created a free online service to compute and visualize the first Brillouin zone, labeled k-points and suggested band paths for any crystal structure, that we made available at http://www.materialscloud.org/tools/seekpath/.

MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems

A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

In Situ Functionalized Polymers for siRNA Delivery.

A new method is reported herein for screening the biological activity of functional polymers across a consistent degree of polymerization and in situ, that is, under aqueous conditions and without purification/isolation of candidate polymers. In brief, the chemical functionality of a poly(acryloyl hydrazide) scaffold was activated under aqueous conditions using readily available aldehydes to obtain amphiphilic polymers. The transport activity of the resulting polymers can be evaluated in situ using model membranes and living cells without the need for tedious isolation and purification steps. This technology allowed the rapid identification of a supramolecular polymeric vector with excellent efficiency and reproducibility for the delivery of siRNA into human cells (HeLa-EGFP). The reported method constitutes a blueprint for the high-throughput screening and future discovery of new polymeric functional materials with important biological applications.

Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure

Metal-organic frameworks (MOFs) are nanoporous materials with exceptional host-guest properties poised for groundbreaking innovations in gas separation applications according to high-throughput (HT) screening data. However, MOF structural libraries are nearly infinite in practice and so statistical and information technology will play a fundamental role in implementing and rationalizing MOF virtual screening. In this work, we apply k-means clustering and archetypal analysis (AA) to identify the truly significant nanoporous structures in a large library of ∼82 000 virtual MOFs. Quantitative structure-property relationship (QSPR) models of the theoretical CO2 and N2 uptake capacities were also developed using a calibration set of ∼16 000 hypothetical MOF structures derived from the prototypes and archetype frameworks. Since uptake capacities correlated poorly to the void fraction, surface area and pore size but these properties were used to build binary classifier predictors that successfully identify "high-performing" nanoporous materials in an external test set of ∼65 000 MOFs with accuracy higher than 94%. The accuracy of the classification decreased for MOFs with fluorine substituents. The classification models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials.

Benchmarking density functional perturbation theory to enable high-throughput screening of materials for dielectric constant and refractive index

We demonstrate a high-throughput density functional perturbation theory (DFPT) methodology capable of screening compounds for their dielectric properties. The electronic and ionic dielectric tensors are calculated for 88 compounds, where the eigenvalues of the total dielectric tensors are compared with single crystal and polycrystalline experimental values reported in the literature. We find that GGA/PBE has a smaller mean average deviation from experiments ($\text{MARD}=16.2$%) when compared to LDA. The prediction accuracy of DFPT is lowest for compounds that exhibit complex structural relaxation effects (e.g., octahedra rotation in perovskites) and/or strong anharmonicity. Despite some discrepancies between DFPT results and reported experimental values, the high-throughput methodology is found to be useful in identifying interesting compounds by ranking. This is demonstrated by the high Spearman correlation factor $(\ensuremath{\rho}=0.92)$. Finally, we demonstrate that DFPT provides a good estimate for the refractive index of a compound without calculating the frequency dependence of the dielectric matrix ($\text{MARD}=5.7$%).

A Compelling Case for Bipolar Electrochemistry

Most electrochemists perform experiments with conventional three-electrode set-ups composed of a working electrode, a counter electrode and a reference electrode. The electrode of interest in that case is the working electrode, where, depending on its polarization with respect to the solution, either an oxidation or a reduction reaction occurs. Things are very different for bipolar electrochemistry, because both oxidation and reduction reactions occur simultaneously on the same electrode and that electrode is not physically connected through an electrical contact to a power supply (see also the tutorial article by R. M. Crooks at the beginning of this special issue1). From a historical point of view, the basic concepts associated with bipolar electrochemistry have been known for several decades. With the advent of micro- and nanotechnology, however, there is considerable renewed interest in this approach. This is because, over the last decade or so, it has become apparent that there are extremely attractive features of bipolar electrochemistry for applications in areas ranging from analytical chemistry to materials science. This impressive recent increase in the number of studies, using bipolar electrochemistry as a basis for the development of intriguing new scientific approaches, is mostly due to four main factors: The electrochemical experiments can be carried out in the absence of an ohmic contact. This wireless feature is certainly the most appealing one and opens the door to many experiments that simply cannot be done with a classic electrochemical set-up. A direct consequence of the preceding point is the fact that the technique allows simultaneous control over thousands or millions of electrodes. This provides access to many new types of experiments, including high-throughput screening of materials and mass production of modified particles. Intrinsically, bipolar electrochemistry is a fantastic tool to break the symmetry of a chemical system in a straightforward way, thus opening perspectives in various fields, ranging from the synthesis of graded, asymmetric or multifunctional materials, to the generation of controlled motion. Last but not least, bipolar electrochemistry can be considered as a low-cost method that generally requires only simple instrumentation that can easily be handled, even by inexperienced persons. The direct consequence of these four advantages is that bipolar electrochemistry has experienced an exponential growth in the number of users (and publications, Figure 1), and we believe that this renaissance will continue as more and more scientists and engineers, in disciplines ranging from chemistry, biology, and materials science to device fabrication, take advantage of “wireless” electrochemistry. Citations of publications with the topic “bipolar electrochemistry” over the last two decades. Adapted from Web of Science. For all these reasons, this is a good time to dedicate a special issue of ChemElectroChem to this topic, gathering some of the most recent achievements based on this concept. We hope that after going through this issue (the first dedicated exclusively to bipolar electrochemistry), the reader will share our enthusiasm for this appealing and straight-forward approach and find new and exciting ways to benefit from the power of this interesting electrochemical method. Alexander Kuhn is Full Professor at the Engineering School of Chemistry, Biology and Physics in Bordeaux (France) and also senior member of the Institut Universitaire de France. His main research interests, documented in over 150 publications, are in highly controlled surface modification of electrodes with applications ranging from (bio)electrocatalysis and analytical chemistry to materials science. He became interested in bipolar electrochemistry ten years ago and since then has been exploring its utility in these different areas together with his collaborators. Richard M. Crooks is presently the Robert A. Welch Chair in Materials Chemistry at The University of Texas at Austin. His scientific interests include electrochemistry, chemical sensing, and catalysis. He has published nearly 300 research papers and is the recipient of several awards, including the Carl Wagner Memorial Award of the Electrochemical Society, the American Chemical Society Electrochemistry Award, and the Faraday Medal of the Royal Society of Chemistry. In addition to his scientific interests, he writes detective fiction and enjoys distance running. Shinsuke Inagi received his Ph.D. from Kyoto University in 2007 under the direction of Prof. Yoshiki Chujo. After postdoctoral research at Kyoto University, he joined the group of Prof. Toshio Fuchigami as an Assistant Professor at Tokyo Institute of Technology in 2007. He was promoted to Lecturer in 2011, then to Associate Professor in 2015. His current research interests focus on organic electrochemistry, including electrochemical synthesis of polymeric materials. Bipolar electrochemistry is definitely extending his research field.

Stability and electronic properties of new inorganic perovskites from high-throughput ab initio calculations

Ab initio high-throughput materials screening of inorganic perovskites yields candidates for applications in photovoltaics, ferroelectrics, magnetoelectrics, and as transparent contacts.

Use of thermographic imaging to screen for drought-tolerant genotypes in Brachypodium distachyon

Thermal imaging has been used to evaluate the response to drought and warm temperatures in a collection of Brachypodium distachyon lines adapted to varied environmental conditions. Thermographic records were able to separate lines from contrasting rainfall regimes. Genotypes from dryer environments showed warmer leaves under water deficit, which suggested that decreased evapotranspiration was related to a more intense stomatal closure. When irrigated and under high temperature conditions, drought-adapted lines showed cooler leaves than lines from wetter zones. The consistent, inverse thermographic response of lines to water stress and heat validates the reliability of this method to assess drought tolerance in this model cereal. It additionally supports the hypothesis that stomatal-based mechanisms are involved in natural variation for drought tolerance in Brachypodium. The study further suggests that these mechanisms are not constitutive but likely related to a more efficient closing response to avoid dehydration in adapted genotypes. Higher leaf temperature under water deficit seems a dependable criterion of drought tolerance, not only in B. distachyon but also in the main cereal crops and related grasses where thermography can facilitate high-throughput preliminary screening of tolerant materials.

Development of a high-throughput screening system for identification of novel reagents regulating DNA damage in human dermal fibroblasts.

Ultraviolet (UV) radiation is a major inducer of skin aging and accumulated exposure to UV radiation increases DNA damage in skin cells, including dermal fibroblasts. In the present study, we developed a novel DNA repair regulating material discovery (DREAM) system for the high-throughput screening and identification of putative materials regulating DNA repair in skin cells. First, we established a modified lentivirus expressing the luciferase and hypoxanthine phosphoribosyl transferase (HPRT) genes. Then, human dermal fibroblast WS-1 cells were infected with the modified lentivirus and selected with puromycin to establish cells that stably expressed luciferase and HPRT (DREAM-F cells). The first step in the DREAM protocol was a 96-well-based screening procedure, involving the analysis of cell viability and luciferase activity after pretreatment of DREAM-F cells with reagents of interest and post-treatment with UVB radiation, and vice versa. In the second step, we validated certain effective reagents identified in the first step by analyzing the cell cycle, evaluating cell death, and performing HPRT-DNA sequencing in DREAM-F cells treated with these reagents and UVB. This DREAM system is scalable and forms a time-saving high-throughput screening system for identifying novel anti-photoaging reagents regulating DNA damage in dermal fibroblasts.

Prediction of the material with highest known melting point fromab initiomolecular dynamics calculations

Using electronic structure calculations, we conduct an extensive investigation into the Hf-Ta-C system, which includes the compounds that have the highest melting points known to date. We identify three major chemical factors that contribute to the high melting temperatures. Based on these factors, we propose a class of materials that may possess even higher melting temperatures and explore it via efficient ab initio molecular dynamics calculations in order to identify the composition maximizing the melting point. This study demonstrates the feasibility of automated and high-throughput materials screening and discovery via ab initio calculations for the optimization of “higher-level” properties, such as melting points, whose determination requires extensive sampling of atomic configuration space.

(Invited) Molecular Understanding of Oxygen Exchange in Solid Oxide Fuel Cell Cathodes

Materials with the ability to rapidly exchange oxygen with the environment play a critical role in many existing and developing technologies, e.g., solid oxide fuel cells (SOFCs), gas separation membranes, oxygen sensors, chemical looping devices, and memristors. In particular, reducing the working temperature of SOFCs, which is critical to their increased commercialization, is significantly inhibited by the slow oxygen exchange kinetics at the cathode. The oxygen exchange is an essential step in the overall oxygen reduction reaction (ORR), where O2 (gas) combines with 4e- to incorporate as 2O2- into the electrolyte, facilitated by the cathode. Oxygen exchange rates are typically quantified by the oxygen exchange coefficient, K*, which represents the response of the rate of oxygen exchange to a driving force. In our work we are developing techniques to predict K* and provide a molecular scale understanding of processes involved in oxygen surface exchange, with the long-term goal of designing more active oxygen exchange materials. Our work has focused on perovskite materials. A few years ago we identified the distance of the bulk oxygen 2p-band center to the Fermi level as an easily calculated descriptor for oxygen exchange in electronic conducing perovskites.[1] These results suggested that overall oxyphilicity plays a critical role in determining K* and provides a tool for high-throughput ab initio screening of promising materials. We will report on recent predictions of active oxygen exchange systems using these screening approaches. In more recent work we have initiated an effort to model oxygen exchange at the molecular level for the (La,Sr)CoO3-d (LSC) system. Using ab inito energetics and the elementary kinetics formalism developed by Adler, et al.[2] We have modeled the possible reaction steps on the LSC (001) surface. This modeling has yielded valuable insights in the rate limiting steps of oxygen exchange through a perovskite surface, the role of surface termination and Sr segregation on K*,[3, 4] and the absolute limits of surface exchange rates. Acknowledgement We gratefully acknowledge financial support from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under award number DE-SC0001284, and computing support from NSF National Center for Supercomputing Applications (NCSA - DMR060007).

Gold-doped graphene: A highly stable and active electrocatalysts for the oxygen reduction reaction.

In addressing the growing need of renewable and sustainable energy resources, hydrogen-fuel-cells stand as one of the most promising routes to transform the current energy paradigm into one that integrally fulfills environmental sustainability. Nevertheless, accomplishing this technology at a large scale demands to surpass the efficiency and enhance the cost-effectiveness of platinum-based cathodes, which catalyze the oxygen reduction reaction (ORR). In this work, our first-principles calculations show that Au atoms incorporated into graphene di-vacancies form a highly stable and cost-effective electrocatalyst that is, at the same time, as or more (dependently of the dopant concentration) active toward ORR than the best-known Pt-based electrocatalysts. We reveal that partial passivation of defected-graphene by gold atoms reduces the reactivity of C dangling bonds and increases that of Au, thus optimizing them for catalyzing the ORR and yielding a system of high thermodynamic and electrochemical stabilities. We also demonstrate that the linear relation among the binding energies of the reaction intermediates assumed in computational high-throughput material screening does not hold, at least for this non-purely transition-metal material. We expect Au-doped graphene to finally overcome the cathode-related challenge hindering the realization of hydrogen-fuel cells as the leading means of powering transportation and portable devices.

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.

Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology

Surface topographies of materials considerably impact cellular behavior as they have been shown to affect cell growth, provide cell guidance, and even induce cell differentiation. Consequently, for successful application in tissue engineering, the contact interface of biomaterials needs to be optimized to induce the required cell behavior. However, a rational design of biomaterial surfaces is severely hampered because knowledge is lacking on the underlying biological mechanisms. Therefore, we previously developed a high-throughput screening device (TopoChip) that measures cell responses to large libraries of parameterized topographical material surfaces. Here, we introduce a computational analysis of high-throughput materiome data to capture the relationship between the surface topographies of materials and cellular morphology. We apply robust statistical techniques to find surface topographies that best promote a certain specified cellular response. By augmenting surface screening with data-driven modeling, we determine which properties of the surface topographies influence the morphological properties of the cells. With this information, we build models that predict the cellular response to surface topographies that have not yet been measured. We analyze cellular morphology on 2176 surfaces, and find that the surface topography significantly affects various cellular properties, including the roundness and size of the nucleus, as well as the perimeter and orientation of the cells. Our learned models capture and accurately predict these relationships and reveal a spectrum of topographies that induce various levels of cellular morphologies. Taken together, this novel approach of high-throughput screening of materials and subsequent analysis opens up possibilities for a rational design of biomaterial surfaces.

High-Throughput Screening of Porous Crystalline Materials for Hydrogen Storage Capacity near Room Temperature

The hydrogen storage capabilities of 18 383 porous crystalline structures possessing various degrees of Mg functionalization and diverse physical properties were assessed through combined grand canonical Monte Carlo (GCMC) and quantum mechanical approaches. GCMC simulations were performed for pressures of 2 and 100 bar at a temperature of 243 K. Absolute uptake at 100 bar and deliverable capacity between 100 and 2 bar were calculated. Maximum absolute and deliverable gravimetric capacities were 9.35 and 9.12 wt %, respectively. Volumetrically, absolute and deliverable capacities were 51 and 30 g/L, respectively. The results reveal relationships between hydrogen uptake and the physical properties of the materials. We show that the introduction of an optimum amount of magnesium alkoxide to increase the isosteric heat of adsorption is a promising strategy to improve hydrogen uptake and delivery near ambient temperature.

Development of scanning electrochemical microscopy (SECM) techniques for the optimization of dye sensitized solar cells

Methods for high-throughput validation of cell components for use in dye sensitized solar cells (DSCs) are needed as the global production of such cells becomes widespread. We have carried out preliminary investigations into the use of scanning electrochemical microscopy (SECM) for high-throughput screening of materials. By coupling the technique of SECM with a light source, we have examined the surface charge of non-earthed pseudo-DSCs under variable light conditions and screened substrates by varying the working electrode potential. By studying the surface currents in a series of tests in which one or more component of a DSC were varied, the effect of TiO2, dye and iodide/triiodide electrolyte on the surface characteristics have been examined.

Highly sensitive porous metal oxide films for early detection of electrical fire: Surface modification and high throughput screening

In this study, an electrical fire simulator was fabricated and connected to a test platform which can take high throughput screening of materials through the method of combinatorial material science. WO3 and In2O3 were used as basis materials in the experiment and modified through the way of micro-injection in order to find the best one sensitive to electrical fire. It is found that CrCl3-modified and MgCl2-modified WO3, as well as LaOCl-modified and SmOCl-modified In2O3 show the best sensing performance. They can be used as sensitive materials for early detection of electrical fire because they have good sensitivity to the smell of PVC even if the working temperature of PVC insulated wires is lower than 200°C. Therefore, it confirms that the early detection of electrical fire in the invisible smoke stage is feasible by using the modified WO3 or In2O3.

Combinatorial Approach to Turbine Bond Coat Discovery

A nondestructive method for the high-throughput screening of novel bond coat materials has been developed. By using a suite of characterization techniques, including Raman spectroscopy, fluorescence spectroscopy, and X-ray diffraction, a rapid determination of thermally grown oxide phases and their protective capability over a continuous composition spread sample can be obtained. The methodology is validated with the Ni-Al system. The procedure developed in this work results in the rapid identification of bond coat composition regions in which the preferred thermally grown oxide, α-Al2O3, is nucleated thus significantly reducing the amount of phase space that needs to be explored in subsequent studies.

A two-electron mechanism of lithium insertion into layered α-MoO3: a DFT and DFT+U study

We study the changes in electrodynamic responses due to Li insertion into layered α-MoO3. We compute electronic and dielectric properties, including the optical conductivity response of pure and lithiated oxides Li2MoO3, as a means to understand and reproduce the charge–discharge profiles observed in experiments. We unravel a 2-electron mechanism of Mo(VI) center reduction, which suggests Li substitution by divalent cations as a route towards cycling optimization. Along this line we provide a general paradigm for battery material characterization using first principles techniques based on optics, which can be advantageously used for example in high-throughput material screening approaches.

Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods

As technologies progress, the development of new mechanical systems demands the rapid determination of friction coefficients of materials. Data mining and materials informatics methods are used here to generate a predictive model that enables efficient high-throughput screening of ceramic materials, some of which are candi- date high-temperature, solid-state lubricants. Through the combination of principal component analysis and recursive partitioning using a small dataset comprised of intrinsic material properties, we develop a decision tree-based model comprised of if-then rules which estimates the friction coefficients of a wide range of materials. This data- driven model has a high degree of accuracy with an R 2 value of 0.8904 and provides a range of possible friction coefficients that accounts for the possible variability of a material's actual friction coefficient.