Surface energies of elemental crystals

By Richard Tran, Zihan Xu, Balachandran Radhakrishnan, Donny Winston, Wenhao Sun, Kristin A. Persson, Shyue Ping Ong. Scientific Data, 2016

890 citations recorded by the source corpus.

The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal’s equilibrium shape. Such surface phenomena are especially important at the nanoscale, where the large surface area to volume ratios lead to properties that are significantly different from the bulk. In this work, we present the largest database of calculated surface energies for elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a maximum Miller index of two and three for non-cubic and cubic crystals, respectively. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous experimental and computational data where available. We will describe the methodology used in constructing the database, and how it can be accessed for further studies and design of materials. Design Type(s) database creation objective Measurement Type(s) surface energy Technology Type(s) solid-state physics Factor Type(s) Design Type(s) database creation objective Measurement Type(s) surface energy Technology Type(s) solid-state physics Factor Type(s) Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Atmospheric Science, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/09deb6d69779bfc1fba96ba5722cb527f1012f32

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In Materials Chemistry

Citations in the corpus

Equilibrium chemistry down to 100 K (2017, methodology): “…rate of crystalline tungsten has been calculated according to classical nucleation theory in the formulation of Gail et al. (1984), with surface tension σ = 3340 erg/cm2 (Tran et al. 2016) and parameter N1/2 = 10 (the cluster size where the surface tension reduces to half its bulk value).”
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation (2018, background): “[59] Richard Tran, Zihan Xu, Balachandran Radhakrishnan, Donald Winston, Wenhao Sun, Kristin A Persson, and Shyue Ping Ong.”
Physically informed artificial neural networks for atomistic modeling of materials (2018, neutral): “[61] R.”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, background): “Such efforts have begun, for example, with the community databases CatApp 91 and crystalium 92 , but substantial improvements to data infrastructure in catalysis are likely necessary to fully realize the potential of AI to accelerate the discovery of useful catalysts 93 .”
Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst (2022, neutral): “These surface terminations were chosen as they represent the low-energy surfaces 64 .”