Researchers from the California Institute of Technology
(Caltech) and Yale University have developed a computational method
that can more accurately predict the Kondo effect in specific
metals, potentially advancing research into complex quantum
materials.
The Kondo effect occurs when magnetic impurities in a metal
cause its electrical resistance to increase as the material is
cooled below a characteristic temperature. Although the underlying
physics has been understood for decades, accurately predicting how
the phenomenon behaves in individual materials has remained a
challenge.
Normally, cooling a metal reduces the thermal vibrations of its
atoms, allowing electrons to move more easily and lowering
electrical resistance. However, when magnetic atoms are present as
impurities, interactions between their localized electrons and the
metal’s conducting electrons can cause resistance to rise at
sufficiently low temperatures.
The researchers addressed this challenge by applying
computational techniques originally developed in quantum chemistry
to the study of metals. Rather than relying on simplified models of
the electronic structure, their approach accounts for the actual
atomic and electronic properties of the material.
The team tested the method on seven different transition-metal
impurities embedded in copper. The calculations reproduced key
trends observed in experiments and, for most of the impurities
studied, outperformed conventional model-based approaches. In some
cases, the improvement in accuracy reached approximately two orders
of magnitude.
The findings, published in the journal Science on July
30, 2026, also provide insight into why the Kondo effect varies
among different magnetic impurities.
Although the method has so far been tested on relatively simple
systems, the researchers say it could help advance computational
studies of more complex materials, including high-temperature
superconductors and quantum magnets.
The study demonstrates how calculations based on the fundamental
properties of real materials could help scientists predict complex
electronic behavior with greater accuracy, potentially reducing
reliance on experimental measurements.
Sources: Original study in Science,Caltech research announcement
