This research is published in R. Jana and M.A. Caro, “Searching for iron nanoparticles with a general-purpose Gaussian approximation potential”, Phys. Rev. B 107, 245421 (2023) [also available from the arXiv]. Reprinted figures are copyright (c) 2023 of the American Physical Society. The video is copyright (c) 2023 of M.A. Caro.
In the field of catalysis it is common to use rare metals because of their superior catalytic properties. For example, platinum and Pt-like metals show the best performance for water splitting, but are too scarce and expensive to be used for many industrial-scale purposes. Instead, research is intensifying on finding alternative solutions based on widely available and cheap materials, especially metallic compounds. (For an overview of different materials specifically for water splitting, see, e.g., the review by Wang et al.)
Of all metallic elements on the Earth’s crust, only aluminium is more abundant than iron. Both metals can be used for structural purposes. However, iron is easier to mine and can be used to make a huge variety of steel alloys with widely varying specifications. For these reasons, iron ore constitutes almost 95% of all industrially mined metal globally. Being an abundant and readily available commodity, the prospect of potentially replacing critical metals with iron is very attractive. This includes developing new Fe-based materials for catalysis.
Some of the main aspects (besides cost and availability) to consider when assessing the prospects of a catalyst material are 1) activity (how much product can we make with a given amount of electrical power), 2) selectivity (whether we make a single product or a mixture of products) and 3) stability (how long does the material and its properties last under operating conditions). For instance, a very active and selective material for the oxygen evolution reaction will not be useful in practice if it has a high tendency to corrode. In that regard, native iron surfaces are not particularly good catalysts. However, there are different ways how this can be tackled. One way to tune the properties of a material is via compositional engineering; i.e., by “alloying” two or more compounds we can produce a resulting compound with quantitatively or even qualitatively different properties compared to the precursors. Another way to tune these properties is by taking advantage of the structural diversity of a compound, because the catalytic activity of a material can be traced back to atomic-scale “active sites”, where the electrochemical reactions take place.
At ambient conditions, bulk (solid) iron has a body-centered cubic (bcc) structure, where every atom has 8 neighbors each at the same distance, and all atomic sites look the same. Iron surfaces have more diversity of atomic sites, depending on the cleavage plane and reconstruction effects. With very thin (nanoscale) surfaces, even the crystal structure can be modified from bcc to face-centered cubic (fcc). In surfaces, the exposed sites differ from those in the bulk, but are still relatively similar to one another (with a handful of characteristic available atomic motifs). However, when we move to nanoparticles (known as “nanoclusters”, when they are very small), ranging from a few to a few hundreds (or perhaps thousands) of atoms, the situation is significantly more complex. For small nanoparticles, the morphology of the available exposed atomic sites will depend very strongly on the size of the nanoparticle. And a single nanoparticle will itself display a relatively large variety of surface sites. And because the atomic environments of these sites are so different, so can also be their catalytic activity. Thus, active sites that are not available in the bulk can be present for the same material in its nanoscale form(s).
To understand and explore the diversity of atomic environments in nanoscale iron, we (mostly Richard Jana with some help from me) developed a new “general-purpose” machine learning potential (MLP) for iron and used it to generate “stable” (i.e., low-energy) iron nanoparticles. Iron is a particularly hard system for MLPs, because of the existence of magnetic degrees of freedom (related to the effective net spins around the iron atoms), in addition to the nuclear degrees of freedom (the “positions” of the atoms). Usually, MLPs (as well as traditional atomistic force fields) are designed to only account explicitly for the latter. For this reason, existing interatomic potentials have been developed to accurately describe the potential energy landscape of “normal” ferromagnetic iron (bcc iron, the stable form at ambient conditions), but fail for other forms, which are relevant at extreme thermodynamic conditions (high pressure and temperature) or at the nanoscale (nanoparticles). While our methodology is still incapable of explicitly accounting for magnetic degrees of freedom, by carefully crafting a general training database we managed to get our iron MLP to implicitly learn the energetics of structurally diverse forms of iron, and in particular managed to achieve very accurate results for small nanoparticles, where the flexibility of a general-purpose MLP is most needed.
We built a catalogue of iron nanoparticles from 3 to 200 atoms, and found structures that were lower in energy than many of those previously available in the literature. Using data clustering techiques, we could identify the most characteristic sites on the nanoparticle surfaces based on their morphological similarities. In the video below, you can see all the lowest-energy nanoparticles we found at each size (in the 3-200 atoms range) with their surface atoms colorcoded according to the 10 most characteristic motifs identified by our algorithm.
The reactivity of each site, e.g., how strongly it can bind an adsorbant, such as a hydrogen atom or a CO molecule, depends very strongly on the surroundings, especially the number of neighbor atoms and how they are arranged. For instance, surface atoms that are almost “buried” inside the nanoparticle are more stable (less reactive) than those which are sticking out and have only few neighbors. Sites that either bind adsorbants too strongly or not at all tend to have poor catalytic activity, whereas sites in between those are the most promising ones because they can transiently bind a reaction intermediate and subsequently release it, allowing the reaction to take place. We have made initial progress in drawing the connection between motif classification and activity based on the MLP predictions, as seen in the featured figure at the top of this page. Navigating this wealth of surface sites and thoroughly screening their potential to catalyze specific chemical reactions (with more application-specific ML models or with DFT) is the next logical step.
We hope that this work will stimulate further research into the catalytic properties of iron-based nanocatalysts and bring us one step closer to the cheap and sustainable development of electrocatalysts for industrial production of fuels and chemicals.
