Combinatorial Materials Science: What's New Since Edison?

A combinatorial materials science method for screening potential elemental additions to mitigate tin whisker formation on Pb-free electronics is presented. This combinatorial method is demonstrated using compositional gradient libraries deposited on glass substrates. Fabrication and heat treatment of combinatorial libraries is discussed. The effectiveness of this high-throughput screening method becomes evident from the analysis of two ternary systems (W-Cr-Ni and Mo-Al-Co) deposited over a tin matrix on glass substrates. Scanning electron microscopy revealed that tin whiskers tend to nucleate and grow on samples with no compressive stresses associated with the formation of intermetallic layers. Additionally, a nickel layer between the glass and tin was found to hinder the formation of whiskers; however, the traditional argument of Ni as a diffusion barrier between Sn-Cu not longer explains the observed behavior. This combinatorial method provides a rapid assessment tool to identify potential elemental mixtures to mitigate tin whiskers while using a simple, cost effective experimental approach.

Using combinatorial and high-throughput materials science methods, we have studied thin-film libraries of and alloy negative electrode materials for Li-ion batteries. Over one hundred compositions have been studied carefully by X-ray diffraction and electrochemical methods. The system is found to be amorphous for . For , the amorphous phase coexists with electrochemically inactive crystalline . Amorphous materials with show a specific capacity of , but differential capacity, , vs potential is not stable vs cycling indicating irreversible atomic-scale changes in the alloy, most likely due to tin aggregation. Adding carbon to this system, for example in the library, has a number of positive effects. First, all alloys with are amorphous, with carbon directly incorporated within the amorphous phase. Second, the addition of carbon increases, not decreases, the specific capacity from about for for . Third, compositions with show differential capacity vs potential curves that do not change during charge-discharge cycling, indicating that such alloys are stable on the atomic scale and hence are extremely good candidates for long cycle life. Stability increases with carbon content up to .

The traditional approach of statistical physics to supervised learning routinely assumes unrealistic generative models for the data: Usually inputs are independent random variables, uncorrelated with their labels. Only recently, statistical physicists started to explore more complex forms of data, such as equally labeled points lying on (possibly low-dimensional) object manifolds. Here we provide a bridge between this recently established research area and the framework of statistical learning theory, a branch of mathematics devoted to inference in machine learning. The overarching motivation is the inadequacy of the classic rigorous results in explaining the remarkable generalization properties of deep learning. We propose a way to integrate physical models of data into statistical learning theory and address, with both combinatorial and statistical mechanics methods, the computation of the Vapnik-Chervonenkis entropy, which counts the number of different binary classifications compatible with the loss class. As a proof of concept, we focus on kernel machines and on two simple realizations of data structure introduced in recent physics literature: k-dimensional simplexes with prescribed geometric relations and spherical manifolds (equivalent to margin classification). Entropy, contrary to what happens for unstructured data, is nonmonotonic in the sample size, in contrast with the rigorous bounds. Moreover, data structure induces a transition beyond the storage capacity, which we advocate as a proxy of the nonmonotonicity, and ultimately a cue of low generalization error. The identification of a synaptic volume vanishing at the transition allows a quantification of the impact of data structure within replica theory, applicable in cases where combinatorial methods are not available, as we demonstrate for margin learning.

After forever changing the drug discovery process in the pharmaceutical industry, combinatorial chemistry methodologies are increasingly being applied to the discovery and optimization of more efficient catalysts and materials (see picture). With the advent of new combinatorial synthesis and screening technologies, coupled with integrated data management systems, the application of these technologies to materials science and catalyst research holds tremendous potential and brings high expectations to this new and exciting field.

Abstract— According to the recent demand for materials for use in various displays and solid‐state lighting, new phosphors with improved performance have been consistently pursued. Multi‐objective genetic‐algorithm‐assisted combinatorial‐material‐search (MOGACMS) strategies have been applied to various multi‐compositional inorganic systems to search for new phosphors and to optimize the properties of phosphors. In addition, the troublesome, complex problem of high‐throughput experimentation (HTE), the inconsistency, which is frequently faced by combinatorial material scientists, is especially emphasized. The luminance and inconsistency was treated as two objective functions in our MOGACMS strategy to pinpoint and optimize promising phosphors with high photoluminance and reliable reproducibility. Using MOGACMS, several multi‐dimensional oxide systems were screened in term of the minimization of inconsistency and the maximization of luminance.

view Abstract Citations (44) References (38) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS Warm Neutral Halos around Molecular Clouds. VI. Physical and Chemical Modeling Andersson, B. -G. ; Wannier, P. G. Abstract A combined physical and chemical modeling of the halos around molecular clouds is presented, with special emphasis on the H-to-H2 transition. On the basis of H I 21 cm observations, it is shown that the halos are extended. A physical model is employed in conjunction with a chemistry code to provide a self-consistent description of the gas. The radiative transfer code provides a check with H I, CO, and OH observations. It is concluded that the warm neutral halos are not gravitationally bound to the underlying molecular clouds and are isobaric. It is inferred from the observed extent of the H I envelopes and the large observed abundance of OH in them that the generally accepted rate for H2 information on grains is too large by a factor of two to three. Publication: The Astrophysical Journal Pub Date: January 1993 DOI: 10.1086/172161 Bibcode: 1993ApJ...402..585A Keywords: Astronomical Models; Astrophysics; Galactic Halos; Interstellar Chemistry; Molecular Clouds; Computational Astrophysics; Hydrogen; Interstellar Gas; Polytropic Processes; Radiative Transfer; Astrophysics; ISM: CLOUDS; ISM: MOLECULES full text sources ADS | data products SIMBAD (11) Related Materials (5) Part 1: 1983ApJ...268..727W Part 2: 1991ApJS...75..987W Part 3: 1991ApJ...366..464A Part 4: 1992A&A...260..355A Part 5: 1993ApJ...407..163W

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Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.

6 - Synthesis and processing

This chapter contains sections titled: Overview Outlook Challenges Summary References

In recent years, digitalization has been recognized as a significant and necessary process that leads to better results, both in the education and in the scientific research. The legal and strategic framework in the Republic of Serbia follows the modern trends of the concept of open science, open knowledge, digital transformation and a unique digital format of scientific production. The Ministry of science, technological development and innovation, in cooperation with the Office for information technology and electronic administration, created the necessary conditions for dynamic the development of the scientific research and innovation system, especially in relation to the digitalization process and the establishment of an information platform, in the form of a unified national information system of scientific research activities: eNauka [eScience]. The concept of eNauka is an innovative tool that improves the efficiency and transparency of scientific processes in Serbia. The eNauka portal in the Republic of Serbia is dedicated to promoting scientific research, innovation and academic achievements within the scientific community of the Republic of Serbia. This portal provides access to scientific articles, research papers and academic publications authored by researchers from Serbia, which enables the spread of knowledge and the encouragement of scientific cooperation within the country. The eNauka portal was opened in full capacity for all active researchers in Serbia on July 3, 2023, and contains data on scientific research organizations in the Republic of Serbia, researchers and their scientific results. The goal of this paper was to bring closer to the academic community the capabilities and performance of the eNauka portal, its structure, the way and importance of data editing, with special reference to issues related to researchers’ profiles and their editing. The paper is methodologically based on a theoretical, preliminary desk analysis of relevant contemporary standpoints in domestic and foreign theory, a normative analysis of current legislative sources, an inductive and deductive approach in researching the operational performance and advantages of the eNauka portal, as well as the quantitative analysis of relevant statistical indicators of relevant parameters regarding the profile that each researcher has on the eNauka portal. The results include the analysis of data related to editing the researcher’s profile and the opportunities offered by the eNauka portal. The conclusion refers to the importance and advantages of the eNauke portal. Finally, it should be noted that one of the key functions of eNauka is the evaluation of scientific results. More precisely, the system provides the possibility of monitoring and evaluating research results, which is of great importance for the academic community.

In the conditions of global competition, the development and realization of original solutions in the process of artistic design, contributing to the creation of products with unique properties, is a promising prospect. On this basis, it is relevant to turn to the combinatorial method in order to obtain multivariant, universal and durable clothing. The methodological basis of the research was formed by general scientific methods of cognition, as well as system and structural-functional approach, art-composition analysis. Based on world practice, the article discusses the combination techniques used by designers. Modern constructive solutions of the structure of clothing models are analyzed, which are based on a module that allows to change the object variably. The task is set to develop the design of the product in the ethno-style based on the application of the investigated method of creative shaping. In this case, the issues of preserving national identity by rethinking traditions as a condition of self-identification in the multipolar world of culture are taken into account. As a result of the research, we came to the conclusion that the combinatorial method offers a range of possibilities for individualization of the image, which is characterized by multifunctionality, variability, and aesthetics. This, in turn, can contribute to the development of ecological thinking, i.e. a meaningful approach to resource consumption. Of particular interest is the formation of the method's interrelation with the philosophy of cut of the traditional Kazakh costume.

Aims.We present ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra. The aim of the modelling framework is to provide a tool able to link observations to physical models of exoplanet atmospheres.Methods.The modelling philosophy chosen in this paper is to use physical and chemical models to constrain certain parameters while leaving certain parts of the model, where our physical understanding remains limited, free to vary. This approach, in between full physical modelling and full parameterisation, allows us to use the processes we understand well and parameterise those less understood. We implemented a Bayesian retrieval framework and applied it to the transit spectra of a set of ten hot Jupiters. The code contains chemistry and cloud formation and has the option for self-consistent temperature structure computations.Results.The code presented is fast and flexible enough to be used for retrieval and for target list simulations for JWST or the ESA Ariel missions for example. We present results for the retrieval of elemental abundance ratios using the physical retrieval framework and compare this to results obtained using a parameterised retrieval setup.Conclusions.We conclude that for most of the targets considered, their elemental abundance ratios cannot be reliably constrained based on the current dataset. We find no significant correlations between different physical parameters. We confirm that planets in our sample with a strong slope in the optical transmission spectrum are those for which we find cloud formation to be most active. Finally, we conclude that with ARCiS we have a computationally efficient tool to analyse exoplanet observations in the context of physical and chemical models.

Summary: For speeding‐up preparation as well as investigating new polymeric materials, combinatorial techniques, parallel experimentation, and high‐throughput screening methods represent a very promising approach in polymer chemistry: a large variety of parameters can be screened simultaneously resulting in new structure–property relationships. As previously described, polymer chemistry seems to be perfectly suited for combinatorial approaches since it is relatively easy to vary many parameters during the synthesis, processing, blending, or compounding. Moreover, the development and application of high‐throughput screening techniques for polymer properties can accelerate the development of new materials and can result in new structure–property relationships. Therefore, these screening tools, together with parallel preparation techniques, will significantly decrease the time to market of new products. Here we provide an update of our recent overview covering new developments in the field of combinatorial and parallel polymer synthesis and high‐throughput screening.Development of high‐throughput screening techniques for polymer properties.magnified imageDevelopment of high‐throughput screening techniques for polymer properties.

A review of high throughput and combinatorial electrochemistry