EPJ B Highlight - Predicting adsorption with quantum indicators

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Published on 27 June 2025

A new mathematical framework predicts how solid materials adsorb to surfaces, using five quantum-based indicators of their constituent atoms

Many areas of research are challenged by the need to explain the properties of materials based on the quantum behaviour of their atoms. One particularly difficult effect to describe is the adsorption of solid materials onto other solid surfaces. Currently, this is often approached using band-based models, which consider groups of electron energy level, or ‘bands’, in the atoms that make up a material. However, these models have limited accuracy, especially when trying to connect quantum details with practical outcomes.

In new research published in EPJ B, Yonghui Li and colleagues at Tianjin University, China, introduce a method that more accurately predicts how strongly atoms in a material will adsorb to other surfaces. Their approach is based on a set of five key indicators, offering a more complete picture of how adsorption is controlled by atomic electron bands. This could have wide-ranging applications in areas such as materials design, catalysis, and biological systems.

Li’s team extended the band-based approach by distinguishing between different types of interaction between atoms in a material and those in a surface. They did this using a mathematical framework called ‘Bonding Decomposition’, which uses limited machine learning support to break down electron interactions into components such as bonding, non-bonding, and anti-bonding states. It also accounts for quantum effects like orbital mixing and ‘repulsive orthogonalisation’, which occurs when orbitals are forbidden from overlapping.

Using this framework, the team could predict adsorption strength based on five indicators: where the electrons are located, how spread out they are, how many are in non-bonding states, and the strength of both bonding and repulsive interactions. Compared to older models, this allowed for more accurate and intuitive predictions about whether adsorption will be strong or weak.