High-throughput calculations of catalytic properties of bimetallic alloy surfaces

Mamun, Osman; Winther, Kirsten T.; Boes, Jacob R.; Bligaard, Thomas

doi:10.1038/s41597-019-0080-z

Cited by 103 publications

(107 citation statements)

References 34 publications

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“…[62][63][64][65][66][67][68] In other areas, including organic synthesis, [69][70][71][72][73] and theoretical [74][75][76][77][78][79][80][81] and inorganic 82,83 chemistry, the use of ML is rapidly growing. In catalysis, 84,85 several examples have been reported for both heterogeneous [86][87][88][89][90][91][92][93] and homogeneous [94][95][96][97][98] systems.…”

Section: Introductionmentioning

confidence: 99%

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

et al. 2020

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“…With the development of high-quality computer code and database management system (DBMS), some rich databases have emerged which are playing a crucial role for the high-throughput materials exploration through materials informatics [17][18][19][20] . To expedite catalysis informatics with such databases, we have generated a database of chemisorption energies on a wide range of catalytic surfaces, available at Catalysis-Hub.org 21,22 . Specifically, we generated a dedicated database of chemisorption energies of single-atom (such as C, H, N, O, and S) and multi-atoms (such as CH, CH 2 , CH 3 , OH, NH, and SH) adsorbates on 2035 binary alloy materials in their A 1 , L1 0 , and L1 2 strukturbericht designation.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

et al. 2020

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For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

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“…To understand or simulate the properties of novel polymorphs of functional materials their crystal structure must first be solved for, which remains a challenging problem in materials science. 1,2 Large high-throughput ab initio datasets [3][4][5][6] have enabled approaching many problems in materials research with machine-learning, 7 but these datasets are systematically biased towards known materials or hypothetical materials derived from common crystal prototypes. Thus, there is a need for the systematic exploration of structural diversity at target elemental compositions.…”

Section: Introductionmentioning

confidence: 99%

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction

Flores

Paolucci²,

Winther³

et al. 2020

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The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach we are able to find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry.

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High-throughput calculations of catalytic properties of bimetallic alloy surfaces

Cited by 103 publications

References 34 publications

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction

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