Abstract:Starting from the experimental data contained in the inorganic crystal structure database, we use a statistical analysis to determine the likelihood that a chemical element A can be replaced by another B in a given structure. This information can be used to construct a matrix where each entry ( ) A B, is a measure of this likelihood. By ordering the rows and columns of this matrix in order to reduce its bandwidth, we construct a one-dimension ordering of the chemical elements, analogous to the famous Pettifor … Show more
“…Early attempts, which predate machine learning, include, e.g., Pettifor structural maps that use elementary properties to separate different binary or ternary structures from each other in a 2D plot, allowing the prediction of new stable structures. [248][249][250][251] In some sense, Pettifor maps are already closely related to recent work, such as ref. 163 , where a structural map for binary structures based on chemical properties was developed with SISSO.…”
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.
“…Early attempts, which predate machine learning, include, e.g., Pettifor structural maps that use elementary properties to separate different binary or ternary structures from each other in a 2D plot, allowing the prediction of new stable structures. [248][249][250][251] In some sense, Pettifor maps are already closely related to recent work, such as ref. 163 , where a structural map for binary structures based on chemical properties was developed with SISSO.…”
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.
“…• Selected Ternary phases: Due to the vast chemical space available for ternary hydrides a systematic search can hardly be afforded and thus only few studies, in literature, explore selected phases and compositions on ternary hydrides. For example, one can focus on ternary compositions stable at ambient pressure or attempt for substitutions in binary hydride based on concepts as chemical substitubility [473,340], or electronic structure arguments.…”
Section: Optimizing Tc and Pressure In Hydridesmentioning
Two hydrogen-rich materials: H 3 S and LaH 10 , synthesized at megabar pressures have revolutionized the field of superconductivity by providing a first glimpse into the solution for the hundred-year-old problem of room-temperature superconductivity. The mechanism governing these exceptional superconductors is the conventional electron-phonon coupling. Here, we describe recent advances in experimental techniques, superconductivity theory and first-principles computational methods, which made this discovery possible. The aim of this work is to provide an up-to-date compendium of the available results on superconducting hydrides and explain the synergy of different methodologies that led to the extraordinary discoveries in the field. Furthermore, in an attempt to evidence empirical rules governing superconductivity in binary hydrides under pressure, we discuss general trends in electronic structure and chemical bonding. The last part of the Review introduces possible strategies to optimize pressure and transition temperatures in conventional superconducting materials. Directions for future research in areas of theory, computational methods and high-pressure experiments are proposed, in order to advance our understanding of superconductivity.
“…In Fig. 2(a,b) the adsorption energies are plotted as a function of metal A and B, that are arranged on an improved Pettifor scale 18,19 , with small adjustments for magnetic elements, which ensures a smooth variation of the adsorption energies with composition. Grey areas in the figure can be seen for structures where converged adsorption energies could not be obtained due to surface reconstruction, mismatch in the magnetic structure of the slab and the adsorbate-slab structure or convergence problems for the electronic structure calculation.Fig.…”
A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CH
x
, OH, NH, and
S
H - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform
www.Catalysis-hub.org
for easy retrieval of the data for further scientific analysis.
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