Using the Computational Pourbaix App

The Materials Project has announced an exciting new tool – the Computational Pourbaix App! This app helps users assess stability in aqueous environments, including multicomponent systems. Below, we briefly introduce Pourbaix diagrams and how the Pourbaix App can be used.

A Pourbaix diagram, also frequently called a potenial-pH diagram, or E-pH diagram, is a representation of solid-aqueous phase electrochemical equilibria. It is a two-dimensional representation of a three-dimensional free energy-pH-potential diagram, depicting the stable phase (or combination of stable phases) as a function of pH and potential. Typically, it also displays the conditions under which water is stable, where it either oxidizes (O2 evolution) or reduces (H2 evolution) – see orange dashed lines in Fig 1.

lines_markedFigure 1: “Fe” computed Pourbaix diagram

The Pourbaix diagram is very useful tool to estimate aqueous stability of solids and for guiding synthesis of solids through precipitation as a function of the aqueous solution. To target certain conditions, the pH and potential of the solution can be modified adding acid (e.g. HCl), alkaline salts (e.g. NaOH) as well as oxidants (e.g. permanganate salts) or reducing agents (e.g. carbon, hydrazine).

The Pourbaix App is capable of plotting elemental (e.g. Ni), and multi-element Pourbaix diagrams (e.g. Ni-Al). The default ion concentration is 10-6 M and note that oxygen and hydrogen are included by default, since these elements are always “open” in a Pourbaix diagram as they are available in the water environment.

Example 1 – single element (Fe)

To know which solid forms of iron are likely to form under normal aqueous conditions, construct the Fe diagram in the Computational Pourbaix App by entering “Fe” in the Element field, and click on the Generate button.

In Figure 1 above you can see that Fe2O3 and Fe3O4 (also called ‘rust’ !) form as the only stable solid phases when Fe reacts with water at potentials higher than 0 V vs the standard hydrogen potential. We also observe (as is the case for many elements and materials) that the stable phases of iron in acid as well as alkaline conditions are dissolved ions (Fe2+, FeO22-, etc).

Example 2 – multi element (Fe-Cr)

Let’s try to generate and interpret a multi-element Pourbaix diagram. For example, enter “Fe Cr” in the Element field, and click the Generate button (Fig 2). Note that the default composition for solids is 50-50, e.g. equal amounts Fe and Cr. The solid composition axis is really a third dimension and we show slices for each composition in the Pourbaix App, depending on what you specify. By moving the bar (boundary) between the Cr (blue) and Fe (green) – you can change the composition rendered.

FeCr_pourbaixFigure 2 – “FeCr” computed Pourbaix diagram

The multi-element Pourbaix diagram shows which phases (or combinations of phases) are stable as a function of aqueous conditions. For example, we can study which element is most prone to dissolve as a function of pH and potential. The passivation region (where only solid phases are stable) extends up to about 1 V and spans the region of 5 < pH and we observe that Cr generally resists dissolution more than Fe. Actually, stainless steels take advantage of this as they contain sufficient chromium to form a passive film of chromium oxide, which then prevents further surface corrosion by blocking oxygen diffusion to the steel surface and blocks corrosion from spreading into the alloy’s internal structure.


pymatgen.org

Pymatgen now has its own domain. Please visit pymatgen.org in future for documentation and tutorials. Also, a new version of pymatgen 2.7.9 have been released in the last week with many bug fixes and improvements.


A materials genome approach to finding new low hole effective mass p-type Transparent Conducting Oxides

Transparent materials (e.g., glass) are bad electrical conductors while the best conductors (e.g., metals) are not transparent. It is therefore remarkable that some materials called transparent conducting oxides or TCOs combine those two properties. TCOs are necessary to many technologies from the touch-screen in your smart-phone to new generation solar cells. There are two types


The Materials App on Google Play

The Materials Project has released its very own Android app! The app allows you to use the Materials and Battery Explorer, Phase Diagram App, and Reaction Calculator right from your Android phone. Follow this link or search for “materials project app” in Google Play (it should be one of the first few results) to download


Major data release!

The Materials Project is happy to announce a major release of new data! Included are: * Over 10,000 new band structures (total of 14,000+) * Search by Kohn-Sham band gap in Materials Explorer * Several thousand additional compounds, as well as removal of duplicate compounds * Provenance information for compounds at bottom of details page


Overview paper published in APL Materials!

The Materials Project has published a general overview paper in the inaugural issue of APL Materials! The paper discusses the project’s goals, structure, and future directions: The Materials Project: A materials genome approach to accelerating materials innovation


pymatgen v2.5.2

Version 2.5.2 of pymatgen has been released! Check out the change log at https://pypi.python.org/pypi/pymatgen for information on the latest changes.


The Materials Project is hiring!

The Materials Project has openings for new postdocs and web developers under the supervision of Dr. Kristin Persson at Lawrence Berkeley National Lab. Details of the positions can be found on the new Materials Project jobs page, which can always be found at jobs.materialsproject.org.


A new year, a new pymatgen

The first new version of pymatgen for 2013 has been released! This version brings many efficiency improvements to the core classes and bug fixes. It also includes a brand new StructureMatcher, which is a vastly improved way of comparing structures for similarity. Check out the new release at the pymatgen PyPI page.


Journal article on the Python Materials Genomics library

The official Python Materials Genomics (pymatgen) journal article has just been published in Computational Materials Science. The article provides a broad overview of the aims of pymatgen, its current capabilities, as well as a few examples of what it can do. Check it out at http://dx.doi.org/10.1016/j.commatsci.2012.10.028 .