Generating information-rich high-throughput experimental materials genomes using functional clustering via multitree genetic programming and information theory

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence-named here the rule of four-has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the rule of four are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.

Active Knowledge Extraction from Cyclic Voltammetry

Cyclic Voltammetry (CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions. In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the characterization settings to obtain such curve are not known a priori. In this paper, an active search framework is defined to accelerate identification of characterization settings that enable knowledge extraction from CV experiments. Towards this goal, a representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either S-shape or not S-shape. Using an active search with BMS oracle, we report a linear target identification in a six-dimensional search space (comprised of thermodynamic, mass transfer, and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts, and other energy conversion and storage systems.

Interfacial catalytic materials; challenge for inorganic synthetic chemistry

Interfacial catalysts are indispensable functional materials in the energy transformation. The traditional empirical search strategies reach their potential. Knowledge-based approaches have not been able to deliver innovative and scalable solutions. Following a short analysis of the origin of these shortcomings a fresh attempt on the material challenge of catalysis is proposed. The approach combines functional understanding of material dynamics derived from operando analysis with digital catalysis science guiding the exploration of non-linear interactions of material genes to catalytic functions. This critically requires the ingenuity of the synthetic inorganic chemist to let us understand the reactivity of well-defined materials under the specific conditions of catalytic operation. It is the understanding of how the kinetics of phase changes brings about and destroys active sites in catalytic materials that forms the basis of realistic material concepts. A rigorous prediction and engineering of these processes may not be possible due to the complexity of options involved.

Towards Experimental Handbooks in Catalysis

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

Metric Learning for High-Throughput Combinatorial Data Sets

Materials design and discovery through the high-throughput exploration of materials space has been recognized as a new paradigm in materials science. However, typical high-throughput exploration methods deliver high-dimensional and very diverse data sets that pose the challenge of extracting the key features and patterns that could guide the discovery process. Unraveling patterns is a nontrivial task as quite often the underlying physical phenomena are uncertain and latent variables governing the performance are mainly unknown. In this paper, we discuss challenges related to designing a data analytics tool for clustering high-throughput measurements performed on the compositional library of materials. The critical aspects of our methodology are (i) learning the similarity measures, as opposed to using fixed similarity measures (e.g., Euclidean distance, dynamic time warping), while (ii) imposing the similarity in the composition space. Our methodology is based on the multitask learning approach that is formulated to account for the composition neighborhoods that are specific to the compositional libraries. We demonstrate the advantages of our methodology for the library of cyclic voltammetry curves generated for model multimetal catalysts, as well as X-ray diffraction patterns from experimental studies. We also compare our approach with the current state-of-the-art methods used in similar problems. This work has important implications for designing high-throughput exploration including catalysts for electrochemical systems, such as fuel cells and metal-air batteries.

Prediction of Ternary Liquidus Temperatures by Statistical Modeling of Binary and Ternary Ag-Al-Sn-Zn Systems

The relationship of liquidus temperatures among six binary and four ternary phases in a Ag-Al-Sn-Zn system was analyzed by means of statistical modeling. Four statistical models to predict changes in the liquidus temperatures in Ag-Al-Sn-Zn were proposed on the basis of different hypotheses derived from macroscopic and microscopic standpoints. The results of interpolation tests to evaluate the prediction accuracies of the ternary liquidus temperatures suggested that the multivariate regression model based on binary liquidus temperatures, interactive binary liquidus temperatures, and products of atomic ratios was found to be the most effective among the four models. It was numerically shown that the prediction accuracies of the liquidus temperatures in local ternary systems of Ag-Al-Sn-Zn can be improved further by using the models identified in their neighboring systems. Finally, the possibility to extract the general trend and the abnormal combination of elements for the prediction of liquidus temperatures was discussed on the basis of the statistical framework we considered.

Automated Phase Segmentation for Large-Scale X-ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm

The creation of composition-processing-structure relationships currently represents a key bottleneck for data analysis for high-throughput experimental (HTE) material studies. Here we propose an automated phase diagram attribution algorithm for HTE data analysis that uses a graph-based segmentation algorithm and Delaunay tessellation to create a crystal phase diagram from high throughput libraries of X-ray diffraction (XRD) patterns. We also propose the sample-pair based objective evaluation measures for the phase diagram prediction problem. Our approach was validated using 278 diffraction patterns from a Fe-Ga-Pd composition spread sample with a prediction precision of 0.934 and a Matthews Correlation Coefficient score of 0.823. The algorithm was then applied to the open Ni-Mn-Al thin-film composition spread sample to obtain the first predicted phase diagram mapping for that sample.

High Throughput Light Absorber Discovery, Part 2: Establishing Structure-Band Gap Energy Relationships

Combinatorial materials science strategies have accelerated materials development in a variety of fields, and we extend these strategies to enable structure-property mapping for light absorber materials, particularly in high order composition spaces. High throughput optical spectroscopy and synchrotron X-ray diffraction are combined to identify the optical properties of Bi-V-Fe oxides, leading to the identification of Bi₄V_1.5Fe_0.5O_10.5 as a light absorber with direct band gap near 2.7 eV. The strategic combination of experimental and data analysis techniques includes automated Tauc analysis to estimate band gap energies from the high throughput spectroscopy data, providing an automated platform for identifying new optical materials.

An apparatus for spatially resolved, temperature dependent reflectance measurements for identifying thermochromism in combinatorial thin film libraries

A metrology and data analysis protocol is described for high throughput determination of thermochromic metal-insulator phase diagrams for lightly substituted VO2 thin films. The technique exploits the abrupt change in near infrared optical properties, measured in reflection, as an indicator of the temperature- or impurity-driven metal-insulator transition. Transition metal impurities were introduced in a complementary combinatorial synthesis process for producing thin film libraries with the general composition space V(1-x-y)M(x)M'(y)O2, with M and M' being transition metals and x and y varying continuously across the library. The measurement apparatus acquires reflectance spectra in the visible or near infrared at arbitrarily many library locations, each with a unique film composition, at temperatures of 1 °C-85 °C. Data collection is rapid and automated; the measurement protocol is computer controlled to automate the collection of thousands of reflectance spectra, representing hundreds of film compositions at tens of different temperatures. A straightforward analysis algorithm is implemented to extract key information from the thousands of spectra such as near infrared thermochromic transition temperatures and regions of no thermochromic transition; similarly, reflectance to the visible spectrum generates key information for materials selection of smart window materials. The thermochromic transition for 160 unique compositions on a thin film library with the general formula V(1-x-y)M(x)M'(y)O2 can be measured and described in a single 20 h experiment. The resulting impurity composition-temperature phase diagrams will contribute to the understanding of metal-insulator transitions in doped VO2 systems and to the development of thermochromic smart windows.

A high-throughput investigation of Fe-Cr-Al as a novel high-temperature coating for nuclear cladding materials

High-temperature alloy coatings that can resist oxidation are urgently needed as nuclear cladding materials to mitigate the danger of hydrogen explosions during meltdown. Here we apply a combination of computationally guided materials synthesis, high-throughput structural characterization and data analysis tools to investigate the feasibility of coatings from the Fe–Cr–Al alloy system. Composition-spread samples were synthesized to cover the region of the phase diagram previous bulk studies have identified as forming protective oxides. The metallurgical and oxide phase evolution were studied via in situ synchrotron glancing incidence x-ray diffraction at temperatures up to 690 K. A composition region with an Al concentration greater than 3.08 at%, and between 20.0 at% and 32.9 at% Cr showed the least overall oxide growth. Subsequently, a series of samples were deposited on stubs and their oxidation behavior at 1373 K was observed. The continued presence of a passivating oxide was confirmed in this region over a period of 6 h.