CO on Pt(111) puzzle; A possible solution

CO adsorption on the Pt(111) surface is studied using first-principles methods. As found in a recent study [Feibelman, et al., J. Phys. Chem. B 105, 4018 (2001)], we find the preferred adsorption site within density functional theory to be the hollow site, whereas experimentally it is found that the top site is preferred. The influence of pseudopotential and exchange-correlation functional error on the CO binding energy and site preference was carefully investigated. We also compare the site preference energy of CO on Pt(111) with the reaction energy of formaldehyde formation from H$_2$ and CO. We show that the discrepancies between the experimental and theoretical results are due to the generalized gradient approximation (GGA) treating different bond orders with varying accuracy. As a result, GGA results will contain significant error whenever bonds of different bond order are broken and formed

Anisotropic Local Correlations and Dynamics in a Relaxor Ferroelectric

Relaxor ferroelectrics have been a focus of intense attention due to their anomalous dielectric characteristics, diffuse phase transitions, and strong piezoelectricity. Understanding the structure and dynamics of relaxors has been one of the long-standing challenges in solid-state physics, with the current model of polar nanoregions in a non-polar matrix providing only a qualitative description of the relaxor phase transitions. In this paper, we investigate the local structure and dynamics in 75%PbMg$_{1/3}$Nb$_{2/3}$O$_3$-25%PbTiO$_3$ (PMN-PT) using molecular dynamics simulations and the dynamic pair distribution function technique. We show for the first time that relaxor transitions can be described by local order parameters. We find that structurally, the relaxor phase is characterized by the presence of highly anisotropic correlations between the local cation displacements. These correlations resemble the hydrogen bond network in water. Our findings contradict the current polar nanoregion model; instead, we suggest a new model of a homogeneous random network of anisotropically coupled dipoles.Comment: We combine our manuscript and supplementary information in one file. 5 pages and 3 figures in main text. 3 pages and 3 figures in supplementary informatio

Accurate construction of transition metal pseudopotentials

We generate a series of pseudopotentials to examine the relationship between pseudoatomic properties and solid-state results. We find that lattice constants and bulk moduli are quite sensitive to eigenvalue, total-energy difference and tail norm errors, and clear correlations emerge. These trends motivate our identification of two criteria for accurate transition metal pseudopotentials. We find that both the preservation of all-electron derivative of tail norm with respect to occupation and the preservation of all-electron derivative of eigenvalue with respect to occupation {[Phys. Rev. B {\bf 48}, 5031 (1993)]} are necessary to give accurate bulk metal lattice constants and bulk moduli. We also show how the fairly wide range of lattice constant and bulk modulus results found in the literature can be easily explained by pseudopotential effects.Comment: submitted to Phys. Rev

Distance-based Analysis of Machine Learning Prediction Reliability for Datasets in Materials Science and Other Fields

Despite successful use in a wide variety of disciplines for data analysis and prediction, machine learning (ML) methods suffer from a lack of understanding of the reliability of predictions due to the lack of transparency and black-box nature of ML models. In materials science and other fields, typical ML model results include a significant number of low-quality predictions. This problem is known to be particularly acute for target systems which differ significantly from the data used for ML model training. However, to date, a general method for characterization of the difference between the predicted and training system has not been available. Here, we show that a simple metric based on Euclidean feature space distance and sampling density allows effective separation of the accurately predicted data points from data points with poor prediction accuracy. We show that the metric effectiveness is enhanced by the decorrelation of the features using Gram-Schmidt orthogonalization. To demonstrate the generality of the method, we apply it to support vector regression models for various small data sets in materials science and other fields. Our method is computationally simple, can be used with any ML learning method and enables analysis of the sources of the ML prediction errors. Therefore, it is suitable for use as a standard technique for the estimation of ML prediction reliability for small data sets and as a tool for data set design