There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The Interaction of Water with Solid Surfaces: Fundamental Aspects Revisited

In comparing a material's resistance to distort under mechanical load rather than to alter in volume, Poisson's ratio offers the fundamental metric by which to compare the performance of any material when strained elastically. The numerical limits are set by ½ and -1, between which all stable isotropic materials are found. With new experiments, computational methods and routes to materials synthesis, we assess what Poisson's ratio means in the contemporary understanding of the mechanical characteristics of modern materials. Central to these recent advances, we emphasize the significance of relationships outside the elastic limit between Poisson's ratio and densification, connectivity, ductility and the toughness of solids; and their association with the dynamic properties of the liquids from which they were condensed and into which they melt.

/pdf/poisson-s-ratio-and-modern-materials-96egici4f6.pdf

Poisson's ratio and modern materials

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

/pdf/recent-advances-and-applications-of-machine-learning-in-55gs85xsws.pdf

Recent advances and applications of machine learning in solid-state materials science

Reference LPI-ARTICLE-1999-017View record in Web of Science Record created on 2006-02-21, modified on 2017-05-12

/pdf/metal-oxide-surfaces-and-their-interactions-with-aqueous-22jt31pax0.pdf

Metal Oxide Surfaces and Their Interactions with Aqueous Solutions and Microbial Organisms.

https://ore.exeter.ac.uk/repository/bitstream/handle/10871/9832/ElAM%20-%20A%20computer%20program%20for%20the%20analysis%20and%20representation%20ofanisotropic%20elastic%20properties.pdf;sequence=2

ElAM: A computer program for the analysis and representation of anisotropic elastic properties ✩

Elastic anisotropy and extreme Poisson's ratios in single crystals

We apply an ab initio methodology to model the low index non-stoichiometric surfaces of \ensuremath{\alpha}-alumina, namely the (0001), $(011\ifmmode\bar\else\textasciimacron\fi{}2),$ $(112\ifmmode\bar\else\textasciimacron\fi{}3),$ $(112\ifmmode\bar\else\textasciimacron\fi{}0),$ $(101\ifmmode\bar\else\textasciimacron\fi{}0),$ $(101\ifmmode\bar\else\textasciimacron\fi{}1),$ and $(224\ifmmode\bar\else\textasciimacron\fi{}3)$ surfaces. We express the stabilities in terms of general surface phase diagrams, which are dependent on temperature and partial pressures of oxygen and hydrogen. Thus, the stable structures and compositions can be evaluated at specific experimental conditions. We show that under ambient conditions there is a common trend, namely only two types of surfaces are thermodynamically stable except at extremes of oxygen or hydrogen partial pressures. These are either fully stoichiometric or fully hydroxylated. We also compare the relative surface stability by generating a Wulff construction. The predicted morphologies compare very well with recent measurements, with the exception of the $(101\ifmmode\bar\else\textasciimacron\fi{}1)$ surface.

Ab initio morphology and surface thermodynamics of α − Al 2 O 3

Molecular dynamics simulations are performed at various temperatures (150-300 K) and coverages (1-3 layers) on the adsorption of water on a clean MgO(100) surface using semiempirical potentials. At the monolayer coverage, a number of very stable (m×n) structures are obtained which differ only by the mutual orientations of the molecules. The p(3×2) phase observed above 180 K in low-energy electron diffraction (LEED) and helium atom scattering (HAS) experiments is shown to be the most stable at 200 K and above this temperature. It contains six inequivalently oriented molecules which lie flat above the cation sites with the hydrogens pointing approximately along the Mg rows. When the water coverage increases, a layer of icelike hexagonal structure within which the water molecules are hydrogen bonded is formed above the stable monolayer. This overlayer, which is stable at 150 K, is not hydrogen bonded to the stable monolayer. At 300 K it tends to break up and to aggregate into a 3D ice structure with strong hydrogen bonding. Examination of the calculated oxygen-oxygen distances dOO in the monolayer and in the icelike overlayer, and the comparison with the correlation diagram of the frequency shift and bandwidth of the water infrared spectrum versus dOO give a very consistent interpretation of the observed polarized infrared signals.

Arnaud Marmier

Papers

ElAM: A computer program for the analysis and representation of anisotropic elastic properties ✩

Elastic anisotropy and extreme Poisson's ratios in single crystals

Ab initio morphology and surface thermodynamics of α − Al 2 O 3

A molecular dynamics study of the structure of water layers adsorbed on MgO(100)

Auxetic frameworks inspired by cubic crystals