Halide-Induced Step Faceting and Dissolution Energetics from Atomistic Machine Learned Potentials on Cu(100)

Groenenboom, Mitchell C.; Moffat, Thomas P.; Schwarz, Kathleen

doi:10.1021/acs.jpcc.0c00683

Cited by 14 publications

(15 citation statements)

References 59 publications

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“…A hkl is the surface area of the (hkl) slab model and L step is the step-edge length along the corresponding direction [⟨110⟩ for the (711) surface and ⟨001⟩ for the (610) surface]. λ ⟨110⟩ and λ ⟨001⟩ were calculated to be 64 and 93 meV/Å, which are consistent with the previous theoretical work 39 and the experimental observations. In the presence of CO* adlattice, the ⟨001⟩ direction step-edge formation becomes more favorable with a λ ⟨001⟩ CO * of 30 meV/Å compared to λ ⟨110⟩ CO * (157 meV/Å) due to the larger stabilization of the (610) surface by CO*.…”

Section: ■ Results and Discussionsupporting

confidence: 85%

Dramatic Change in the Step Edges of the Cu(100) Electrocatalyst upon Exposure to CO: Operando Observations by Electrochemical STM and Explanation Using Quantum Mechanical Calculations

et al. 2021

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Systematic structure–activity correlations in the electrochemical surface science of CO2 reduction (CO2R) are typically anchored on low Miller-index (hkl) surfaces with characterization directed at the dominant well-defined basal planes. The present investigation focused on the visualization of the step edges of unreconstructed Cu(100), with and without CO dissolved in 0.1 M KOH, at the early onset-potential region of CO2R. Operando electrochemical scanning tunneling microscopy revealed that the step-edge direction changed dramatically upon the adsorption–desorption of CO at potentials of −1.0 to −0.8 V. Quantum mechanical calculations corroborated the favorable transformation of the step-edge direction from ⟨110⟩ to ⟨001⟩ as the pristine surface was decorated by CO.

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Section: ■ Results and Discussionsupporting

confidence: 85%

Dramatic Change in the Step Edges of the Cu(100) Electrocatalyst upon Exposure to CO: Operando Observations by Electrochemical STM and Explanation Using Quantum Mechanical Calculations

et al. 2021

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“…Apart from bulk solids, also surfaces have received a lot of attention . HDNNPs have been used to study adsorbate structures including efficient saddle-point searches and adsorbate-induced facetting of surfaces, the morphology of supported nanoparticles and clusters, ,,, and molecule–surface scattering processes. − In the latter field, the use of HDNNPs has finally enabled overcoming the limitation to frozen surfaces in atomistic simulations, thus allowing the inclusion of the role of surface temperature and mobility in gas-surface dynamics, a long-standing problem in the surface science community.…”

Section: Discussionmentioning

confidence: 99%

Four Generations of High-Dimensional Neural Network Potentials

2021

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Since their introduction about 25 years ago, machine learning (ML) potentials have become an important tool in the field of atomistic simulations. After the initial decade, in which neural networks were successfully used to construct potentials for rather small molecular systems, the development of high-dimensional neural network potentials (HDNNPs) in 2007 opened the way for the application of ML potentials in simulations of large systems containing thousands of atoms. To date, many other types of ML potentials have been proposed continuously increasing the range of problems that can be studied. In this review, the methodology of the family of HDNNPs including new recent developments will be discussed using a classification scheme into four generations of potentials, which is also applicable to many other types of ML potentials. The first generation is formed by early neural network potentials designed for low-dimensional systems. High-dimensional neural network potentials established the second generation and are based on three key steps: first, the expression of the total energy as a sum of environmentdependent atomic energy contributions; second, the description of the atomic environments by atom-centered symmetry functions as descriptors fulfilling the requirements of rotational, translational, and permutation invariance; and third, the iterative construction of the reference electronic structure data sets by active learning. In third-generation HDNNPs, in addition, long-range interactions are included employing environment-dependent partial charges expressed by atomic neural networks. In fourth-generation HDNNPs, which are just emerging, in addition, nonlocal phenomena such as long-range charge transfer can be included. The applicability and remaining limitations of HDNNPs are discussed along with an outlook at possible future developments.

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“…A large number of IAP workflows rely on neural networks in order to approximate energies and forces from atomic positions [331], [332], [333], [334], [335], [336], [337], [338], [339], [340], [341], [342], [343], [344], [345], [346], [347], [348], [349], [350], [351], [352], [353], [354], [316], [355], [356], [357], [358], [359], [360], [361], [362], [363], [364], [365], [366], [367], [368].…”

Section: Neural Network Potentialsmentioning

confidence: 99%

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Fiedler¹,

Shah²,

Bußmann³

et al. 2021

Preprint

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With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.

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Halide-Induced Step Faceting and Dissolution Energetics from Atomistic Machine Learned Potentials on Cu(100)

Cited by 14 publications

References 59 publications

Dramatic Change in the Step Edges of the Cu(100) Electrocatalyst upon Exposure to CO: Operando Observations by Electrochemical STM and Explanation Using Quantum Mechanical Calculations

Dramatic Change in the Step Edges of the Cu(100) Electrocatalyst upon Exposure to CO: Operando Observations by Electrochemical STM and Explanation Using Quantum Mechanical Calculations

Four Generations of High-Dimensional Neural Network Potentials

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

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