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Register a new userSymbolic Regression Discovery of New Perovskite Catalysts with High Oxygen Evolution Reaction Activity
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Symbolic regression (SR) is an emerging method for building analytical formulas to find models that best fit data sets. Here, SR was used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. An unprecedentedly simple descriptor, {mu}/t, where {mu} and t are the octahedral and tolerance factors, respectively, was identified, which accelerated the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesized five new oxide perovskites and characterized their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, outperform the current state-of-the-art oxide perovskite catalyst, Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF). Our results demonstrate the potential of SR for accelerating data-driven design and discovery of new materials with improved properties.
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It is shown that producing PrBaCo2O5 and Ba0.5Sr0.5Co0.8Fe0.2O3 nanoparticle by a scalable synthesis method leads to high mass activities for the oxygen evolution reaction with outstanding improvements by 10 and 50 times, respectively, compared to those prepared via the state of the art synthesis method. Here, detailed comparisons at both laboratory and industrial scales show that Ba0.5Sr0.5Co0.8Fe0.2O3 appears to be the most active and stable perovskite catalyst under alkaline conditions, while PrBaCo2O6 reveals thermodynamic instability described by the density functional theory based Pourbaix diagrams highlighting cation dissolution under oxygen evolution conditions. Operando Xray absorption spectroscopy is used in parallel to monitor electronic and structural changes of the catalysts during oxygen evolution reaction. The exceptional BSCF functional stability can be correlated to its thermodynamic metastability under oxygen evolution conditions as highlighted by Pourbaix diagram analysis. BSCF is able to dynamically self reconstruct its surface, leading to formation of Co based oxyhydroxide layers while retaining its structural stability. Differently, PBCO demonstrates a high initial oxygen evolution reaction activity while it undergoes a degradation process considering its thermodynamic instability under oxygen evolution conditions as anticipated by its Pourbaix diagram. Overall, this work demonstrates a synergetic approach of using both experimental and theoretical studies to understand the behavior of perovskite catalysts.
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.
The d-band center descriptor based on the adsorption strength of adsorbate has been widely used in understanding and predicting the catalytic activity in various metal catalysts. However, its applicability is unsure for the single-atom-anchored two-dimensional (2D) catalysts. Here, taking the hydrogen (H) adsorption on the single-atom-anchored 2D basal plane as example, we examine the influence of orbitals interaction on the bond strength of hydrogen adsorption. We find that the adsorption of H is formed mainly via the hybridization between the 1s orbital of H and the vertical dz2 orbital of anchored atoms. The other four projected d orbitals (dxy/dx2-y2, dxz/dyz) have no contribution to the H chemical bond. There is an explicit linear relation between the dz2-band center and the H bond strength. The dz2-band center is proposed as an activity descriptor for hydrogen evolution reaction (HER). We demonstrate that the dz2-band center is valid for the single-atom active sites on a single facet, such as the basal plane of 2D nanosheets. For the surface with multiple facets, such as the surface of three-dimensional (3D) polyhedral nanoparticles, the d-band center is more suitable.
Critical to the development of improved solid oxide fuel cell (SOFC) technology are novel compounds with high oxygen reduction reaction (ORR) catalytic activity and robust stability under cathode operating conditions. Approximately 2145 distinct perovskite compositions are screened for potential use as high activity, stable SOFC cathodes, and it is verified that the screening methodology qualitatively reproduces the experimental activity, stability, and conduction properties of well-studied cathode materials. The calculated oxygen p-band center is used as a first principle-based descriptor of the surface exchange coefficient (k*), which in turn correlates with cathode ORR activity. Convex hull analysis is used under operating conditions in the presence of oxygen, hydrogen, and water vapor to determine thermodynamic stability. This search has yielded 52 potential cathode materials with good predicted stability in typical SOFC operating conditions and predicted k* on par with leading ORR perovskite catalysts. The established trends in predicted k* and stability are used to suggest methods of improving the performance of known promising compounds. The material design strategies and new materials discovered in the computational search help enable the development of high activity, stable compounds for use in future solid oxide fuel cells and related applications.
NiFe oxyhydroxide is one of the most promising oxygen evolution reaction (OER) catalysts for renewable hydrogen production, and deciphering the identity and reactivity of the oxygen intermediates on its surface is a key challenge but is critical to understanding the OER mechanism as well as designing water-splitting catalysts with higher efficiencies. Here, we screened and utilized in situ reactive probes that can selectively target specific oxygen intermediates with high rates to investigate the OER intermediates and pathway on NiFe oxyhydroxide. Most importantly, the oxygen atom transfer (OAT) probes (e.g. 4-(Diphenylphosphino) benzoic acid) could efficiently inhibit the OER kinetics by scavenging the OER intermediates, exhibiting lower OER currents, larger Tafel slopes and larger kinetic isotope effect values, while probes with other reactivities demonstrated much smaller effects. Combining the OAT reactivity with electrochemical kinetic and operando Raman spectroscopic techniques, we identified a resting Fe=O intermediate in the Ni-O scaffold and a rate-limiting O-O chemical coupling step between a Fe=O moiety and a vicinal bridging O. DFT calculation further revealed a longer Fe=O bond formed on the surface and a large kinetic energy barrier of the O-O chemical step, corroborating the experimental results. These results point to a new direction of liberating lattice O and expediting O-O coupling for optimizing NiFe-based OER electrocatalyst.
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