Scientists from Caltech and Yale University have created a computational method to accurately calculate the Kondo effect in specific real materials, moving beyond simplified models.
Scientists at the California Institute of Technology (Caltech) and Yale University have developed a new computational method to precisely calculate the Kondo effect in specific real materials. The research, published in the journal Science, moves away from simplified theoretical models to use the actual atomic and electronic structures of materials, allowing for more accurate predictions of their behavior.
The Kondo effect is a quantum phenomenon that occurs when a single magnetic atom, such as iron or manganese, is embedded as an impurity inside a metal like copper. In ordinary metals, electrical resistance decreases steadily as the material cools. However, in metals with magnetic impurities, resistance stops falling when the material reaches a specific threshold known as the Kondo temperature. At this point, resistance reaches a minimum and then begins to rise again as the temperature continues to drop.
This behavior results from the interaction between the electrons of the magnetic impurity and the electrons traveling through the bulk metal. As the material cools, electrons in the metal interact strongly with the impurity's spin, forming a cloud that screens, or cancels out, the magnetism of the atom. Because this process involves a large number of interacting particles, the Kondo effect is considered a classic many-body quantum problem.
For decades, researchers relied on simplified models that reduced the electronic structure of materials to a small set of orbitals and used approximate mathematical models. These approaches could not reliably calculate the precise temperature at which resistance changes for a specific impurity or predict the exact behavior of real materials.
The new method adapts highly accurate computational tools originally developed in quantum chemistry for describing molecules. By applying these tools to quantum materials, the researchers were able to represent magnetic impurities almost as if they were molecules while retaining the full complexity of their electronic interactions.
The research team tested their method on seven different transition-metal atoms embedded in copper. According to the study, the calculations were as much as two orders of magnitude more accurate than predictions obtained from conventional model-based techniques for most of the elements examined.
Garnet Chan, Bren Professor of Chemistry at Caltech and the senior author of the paper, stated, "It is now possible to predict the properties of some complicated materials purely through computation without referring to experiment."
Linqing Peng, a lead author of the paper, noted the significance of this advancement for future material design. "We are in an exciting era in which faithful predictive quantum descriptions of the full chemical complexity of real materials are coming within reach," Peng said. "It is becoming realistic to predict material-specific behavior of correlated electrons from first principles, even in some of the most challenging classes of quantum materials."
This development provides a pathway for realistic computer simulations of more complicated quantum materials, including high-temperature superconductors and quantum magnets. In these systems, the behavior of electrons is strongly influenced by their interactions with nearby electrons, making them difficult to describe with conventional approximations. By enabling predictive theory to focus the experimental search for new materials, this method could accelerate the discovery of materials with functions emerging from intricate correlated physics.
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This article was drafted with AI assistance and checked against the sources above. Company claims are reported as claims. Cover image is AI-generated.
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