Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO2) into the atmosphere threaten to shift the CO2 cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO2 to fuels and other useful chemicals. We demonstrate that an electron transfer to the π-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO2 activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO2 conversion, and a recipe to find more

Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO$_2$ to fuels and other useful chemicals. We demonstrate that an electron transfer to the $\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO$_2$ activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find more

Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery

In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as data-centric models have been developed for a faster evaluation of adsorption energies compared to first-principles calculations. However, global models designed to describe as many materials as possible might overlook the very few compounds that have the appropriate adsorption properties to be suitable for a given catalytic process. Here, the subgroup-discovery (SGD) local artificial-intelligence approach is used to identify the key descriptive parameters and constrains on their values, the so-called SG rules, which particularly describe transition-metal surfaces with outstanding adsorption properties for the oxygen reduction and evolution reactions. We start from a data set of 95 oxygen adsorption energy values evaluated by density-functional-theory calculations for several monometallic surfaces along with 16 atomic, bulk and surface properties as candidate descriptive parameters. From this data set, SGD identifies constraints on the most relevant parameters describing materials and adsorption sites that (i) result in O adsorption energies within the Sabatier-optimal range required for the oxygen reduction reaction and (ii) present the largest deviations from the linear scaling relations between O and OH adsorption energies, which limit the performance in the oxygen evolution reaction. The SG rules not only reflect the local underlying physicochemical phenomena that result in the desired adsorption properties but also guide the challenging design of alloy catalysts

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we consider weighted combinations, even for linear combinations. Hence, an obvious question is whether we can automate the search for interesting patterns and visualizations. In this paper, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. For example, to understand the distribution of crime rates in different geographic areas in terms of other (numerical, ordinal and/or categorical) variables that describe the areas. We introduce a method to find subgroups in the data that are maximally informative (in the formal Information Theoretic sense) with respect to a single or set of real-valued target attributes. The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes. The approach is based on the Subjective Interestingness framework FORSIED to enable the use of prior knowledge when finding most informative non-redundant patterns, and hence the method also supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio

Learning Rules for Materials Properties and Functions

In materials science and engineering, one is typically searching for materials that exhibit exceptional performance for a certain function, and the number of these materials is extremely small. Thus, statistically speaking, we are interested in the identification of *rare phenomena*, and the scientific discovery typically resembles the proverbial hunt for the needle in a haystack

Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups

Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time

Double-blind test program for astrometric planet detection with Gaia

We use detailed simulations of the Gaia observations of synthetic planetary systems and develop and utilize independent software codes in double-blind mode to analyze the data, including statistical tools for planet detection and different algorithms for single and multiple Keplerian orbit fitting that use no a priori knowledge of the true orbital parameters of the systems. 1) Planets with astrometric signatures $\alpha\simeq 3$ times the single-measurement error $\sigma_\psi$ and period $P\leq 5$ yr can be detected reliably, with a very small number of false positives. 2) At twice the detection limit, uncertainties in orbital parameters and masses are typically $15$. 3) Over 70% of two-planet systems with well-separated periods in the range $0.2\leq P\leq 9$ yr, $2\leq\alpha/\sigma_\psi\leq 50$, and eccentricity $e\leq 0.6$ are correctly identified. 4) Favorable orbital configurations have orbital elements measured to better than 10% accuracy $> 90$ of the time, and the value of the mutual inclination angle determined with uncertainties \leq 10^{\degr}. 5) Finally, uncertainties obtained from the fitting procedures are a good estimate of the actual errors. Extrapolating from the present-day statistical properties of the exoplanet sample, the results imply that a Gaia with $\sigma_\psi$ = 8 $\mu$as, in its unbiased and complete magnitude-limited census of planetary systems, will measure several thousand giant planets out to 3-4 AUs from stars within 200 pc, and will characterize hundreds of multiple-planet systems, including meaningful coplanarity tests. Finally, we put Gaia into context, identifying several areas of planetary-system science in which Gaia can be expected to have a relevant impact, when combined with data coming from other ongoing and future planet search programs.Comment: 32 pages, 24 figures, 6 tables. Accepted for pubolication in A&

Asteroid families classification: exploiting very large data sets

The number of asteroids with accurately determined orbits increases fast. The catalogs of asteroid physical observations have also increased, although the number of objects is still smaller than in the orbital catalogs. We developed a new approach to the asteroid family classification by combining the Hierarchical Clustering Method (HCM) with a method to add new members to existing families. This procedure makes use of the much larger amount of information contained in the proper elements catalogs, with respect to classifications using also physical observations for a smaller number of asteroids. Our work is based on the large catalog of the high accuracy synthetic proper elements (available from AstDyS). We first identify a number of core families; to these we attribute the next layer of smaller objects. Then, we remove all the family members from the catalog, and reapply the HCM to the rest. This gives both halo families which extend the core families and new independent families, consisting mainly of small asteroids. These two cases are discriminated by another step of attribution of new members and by merging intersecting families. By using information from absolute magnitudes, we take advantage of the larger size range in some families to analyze their shape in the proper semimajor axis vs. inverse diameter plane. This leads to a new method to estimate the family age (or ages). The results from the previous steps are then analyzed, using also auxiliary information on physical properties including WISE albedos and SDSS color indexes. This allows to solve some difficult cases of families overlapping in the proper elements space but generated by different collisional events. We analyze some examples of cratering families (Massalia, Vesta, Eunomia) which show internal structures, interpreted as multiple collisions. We also discuss why Ceres has no family

KP solitons in shallow water

The main purpose of the paper is to provide a survey of our recent studies on soliton solutions of the Kadomtsev-Petviashvili (KP) equation. The classification is based on the far-field patterns of the solutions which consist of a finite number of line-solitons. Each soliton solution is then defined by a point of the totally non-negative Grassmann variety which can be parametrized by a unique derangement of the symmetric group of permutations. Our study also includes certain numerical stability problems of those soliton solutions. Numerical simulations of the initial value problems indicate that certain class of initial waves asymptotically approach to these exact solutions of the KP equation. We then discuss an application of our theory to the Mach reflection problem in shallow water. This problem describes the resonant interaction of solitary waves appearing in the reflection of an obliquely incident wave onto a vertical wall, and it predicts an extra-ordinary four-fold amplification of the wave at the wall. There are several numerical studies confirming the prediction, but all indicate disagreements with the KP theory. Contrary to those previous numerical studies, we find that the KP theory actually provides an excellent model to describe the Mach reflection phenomena when the higher order corrections are included to the quasi-two dimensional approximation. We also present laboratory experiments of the Mach reflection recently carried out by Yeh and his colleagues, and show how precisely the KP theory predicts this wave behavior.Comment: 50 pages, 25 figure

Properties of hot and dense matter from relativistic heavy ion collisions

We review the progress achieved in extracting the properties of hot and dense matter from relativistic heavy ion collisions at the relativistic heavy ion collider (RHIC) at Brookhaven National Laboratory and the large hadron collider (LHC) at CERN. We focus on bulk properties of the medium, in particular the evidence for thermalization, aspects of the equation of state, transport properties, as well as fluctuations and correlations. We also discuss the in-medium properties of hadrons with light and heavy quarks, and measurements of dileptons and quarkonia. This review is dedicated to the memory of Gerald E. Brown