A method has been discovered to easily predict the structure of materials using intuitive information such as the electrical properties or size of atoms without complex computational calculations. The efficiency of exploring new materials is expected to improve.
The National Research Foundation of Korea (NRF) announced on the 21st that a research team led by Professor Shim Woo-young of the Department of Materials Science and Engineering at Yonsei University has developed a phenomenological model that can predict the structure of compounds without computational calculations, and successfully produced semiconductor materials using it. A phenomenological model is one that quantifies and predicts complex physical phenomena based on intuitive and empirical indicators.
Predicting the structure of compounds generally relies on computer simulations. This method has high accuracy but carries substantial expenses, requires specialized personnel, and is limited in the research subjects it can handle. Researchers used a phenomenological model, which utilizes intuitive atomic properties such as ionic radius and charge to predict material structures as an alternative.
However, the existing models could only predict simple compounds made up of two elements, making it challenging to analyze compounds with three or more mixed elements. Recently, new material development has focused on complex compositions of three or more elements, necessitating the development of new predictive models suitable for this.
The research team recently developed a new phenomenological model that can intuitively predict the structure of ternary compounds made of 'alkali metals, group 13 (III), and group 15 (V) elements', which are gaining attention as semiconductor and optical materials. This model quantifies chemical bonding characteristics by combining information such as electronegativity and ionic size, allowing predictions of structures based on how these elements bond.
Using this model, the research team accurately classified 35 types of known layered structure materials and found 9 unrecognized new material candidates. Among them, they directly synthesized two materials (K₂In₂P3 and Na₂In₂As₃) known to be semiconductor candidates, and experimentally verified through X-ray and electron microscope analysis that the structures of these materials matched those predicted by the new model.
They also successfully created a two-dimensional material (InAs) using Na₂In₂As₃ and conducted experiments on a next-generation memory device called a memtransistor. A memtransistor is a device based on semiconductor materials containing cations.
Professor Shim Woo-young noted, "This study is the first case to prove that structural predictions based on bonding characteristics are possible in ternary compounds," adding, "It is significant that we presented a lightweight predictive model that can efficiently determine whether a layered structure forms without relying on expensive calculations." Co-first authors Won Jong-beom and Gen.Kim Tae-young expressed hope that "the new model will enhance the efficiency of material exploration, enabling various applications such as new material design and discovery of layered structures for optoelectronics and memory devices."
The research findings were published in the international journals 'Advanced Materials' on May 19 and 'Nature Communications' on July 1.
References
Advanced Materials (2025), DOI: https://doi.org/10.1002/adma.202500056
Nature Communications (2025), DOI: https://doi.org/10.1038/s41467-025-60739-9