Fritz Haber Institute of the Max Planck Society

About: Fritz Haber Institute of the Max Planck Society is a facility organization based out in Berlin, Germany. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 3490 authors who have published 5017 publications receiving 183731 citations. The organization is also known as: Fritz Haber Institute of the Max Planck Society.

Machine learning in materials informatics: recent applications and prospects

Abstract: Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

Atomic-scale structure and catalytic reactivity of the RuO(2)(110) surface

Abstract: The structure of RuO(2)(110) and the mechanism for catalytic carbon monoxide oxidation on this surface were studied by low-energy electron diffraction, scanning tunneling microscopy, and density-functional calculations. The RuO(2)(110) surface exposes bridging oxygen atoms and ruthenium atoms not capped by oxygen. The latter act as coordinatively unsaturated sites-a hypothesis introduced long ago to account for the catalytic activity of oxide surfaces-onto which carbon monoxide can chemisorb and from where it can react with neighboring lattice-oxygen to carbon dioxide. Under steady-state conditions, the consumed lattice-oxygen is continuously restored by oxygen uptake from the gas phase. The results provide atomic-scale verification of a general mechanism originally proposed by Mars and van Krevelen in 1954 and are likely to be of general relevance for the mechanism of catalytic reactions at oxide surfaces.

Understanding activity and selectivity of metal-nitrogen-doped carbon catalysts for electrochemical reduction of CO 2

Abstract: Direct electrochemical reduction of CO2 to fuels and chemicals using renewable electricity has attracted significant attention partly due to the fundamental challenges related to reactivity and selectivity, and partly due to its importance for industrial CO2-consuming gas diffusion cathodes. Here, we present advances in the understanding of trends in the CO2 to CO electrocatalysis of metal- and nitrogen-doped porous carbons containing catalytically active M–N x moieties (M = Mn, Fe, Co, Ni, Cu). We investigate their intrinsic catalytic reactivity, CO turnover frequencies, CO faradaic efficiencies and demonstrate that Fe–N–C and especially Ni–N–C catalysts rival Au- and Ag-based catalysts. We model the catalytically active M–N x moieties using density functional theory and correlate the theoretical binding energies with the experiments to give reactivity-selectivity descriptors. This gives an atomic-scale mechanistic understanding of potential-dependent CO and hydrocarbon selectivity from the M–N x moieties and it provides predictive guidelines for the rational design of selective carbon-based CO2 reduction catalysts. Inexpensive and selective electrocatalysts for CO2 reduction hold promise for sustainable fuel production. Here, the authors report N-coordinated, non-noble metal-doped porous carbons as efficient and selective electrocatalysts for CO2 to CO conversion.

Machine learning of accurate energy-conserving molecular force fields

Abstract: Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol-1 for energies and 1 kcal mol-1 A-1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

Surface-Modified Carbon Nanotubes Catalyze Oxidative Dehydrogenation of n-Butane

Abstract: Butenes and butadiene, which are useful intermediates for the synthesis of polymers and other compounds, are synthesized traditionally by oxidative dehydrogenation (ODH) of n-butane over complex metal oxides. Such catalysts require high O2/butane ratios to maintain the activity, which leads to unwanted product oxidation. We show that carbon nanotubes with modified surface functionality efficiently catalyze the oxidative dehydrogenation of n-butane to butenes, especially butadiene. For low O2/butane ratios, a high selectivity to alkenes was achieved for periods as long as 100 hours. This process is mildly catalyzed by ketonic CO groups and occurs via a combination of parallel and sequential oxidation steps. A small amount of phosphorus greatly improved the selectivity by suppressing the combustion of hydrocarbons.