From left to right, these diagrams show an oxygen atom bonding with a metal, a metal oxide and a perovskite. The new model could help chemical engineers design versions of these three types of catalyst for improving the sustainability of fuel and fertilizer production and the manufacture of household chemicals. Credit: Jacques Esterhuizen, Linic Lab, University of Michigan.In a finding that could help pave the way for cleaner fuels and a more sustainable chemical industry, researchers at the University of Michigan have developed a machine learning model for predicting how the compositions of metal alloys and metal oxides affect their electronic structures.
The electronic structure is key to understanding how a material will perform as a mediator, or catalyst, of chemical reactions. "We're learning to identify the fingerprints of materials and connect them with the material's performance," said Bryan Goldsmith, assistant professor of chemical engineering.
A better ability to predict which metal and metal oxide compositions are best for guiding which reactions could improve large-scale chemical processes such as the production of hydrogen, fuels and fertilizers, and the manufacture of household chemicals such as dish soap.
"The objective of our research is to develop predictive models that will connect the geometry of a catalyst to its performance. Such models are central for the design of new catalysts for critical chemical transformations," explained Suljo Linic, professor of chemical engineering.
One of the main approaches for predicting how a material will behave as a potential mediator of a chemical reaction is to analyze its electronic structure, specifically the density of states. This describes how many quantum states are available to the electrons in the reacting molecules and the energies of those states.
Usually, the electronic density of states is described with summary statistics – an average energy or a skew that reveals whether more electronic states are above or below the average.
"That's OK, but those are just simple statistics," said Goldsmith. "You might miss something. With principal component analysis, you just take in everything and find what's important. You're not just throwing away information."
Principal component analysis (PCA) is a classic machine learning method, taught in introductory data science courses. The researchers used the electronic density of states as input for their PCA model, as the density of states is a good predictor for how a catalyst's surface will adsorb, or bond with, atoms and molecules that serve as reactants. The model links the density of states with the composition of the material.
Unlike conventional machine learning, which is essentially a black box that inputs data and offers predictions in return, the team developed an algorithm that they could understand. "We can see systematically what is changing in the density of states and correlate that with geometric properties of the material," said Jacques Esterhuizen, a doctoral student in chemical engineering and first author of a paper on this work in Chem Catalysis.
This information can help chemical engineers to design metal alloys with the density of states necessary for mediating a chemical reaction. The model accurately reflects correlations already observed between a material's composition and its density of states, as well as turning up new potential trends to be explored.
The model simplifies the density of states into two factors, or principal components. One component essentially covers how the atoms of the metal fit together. In a layered metal alloy, this includes whether the subsurface metal is pulling the surface atoms apart or squeezing them together, and the number of electrons that the subsurface metal contributes to bonding. The other component is just the number of electrons that the surface metal atoms can contribute to bonding. From these two principal components, the researchers can reconstruct the density of states in the material.
This concept also works for the reactivity of metal oxides. In this case, the concern is the ability of oxygen to interact with atoms and molecules, which is related to how stable the surface oxygen is. Stable surface oxygens are less likely to react, whereas unstable surface oxygens are more reactive. The model accurately captured the oxygen stability in metal oxides and perovskites, a special class of metal oxides.
This story is adapted from material from the University of Michigan, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.