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An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal
George Agbeworvi1, Nitin Kumar2, John D Ponis1
1Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States.
Journal of the American Chemical Society
|May 13, 2026
Summary
Researchers engineered neuromorphic computing devices by precisely controlling material structures. This work enables stable artificial neurons with tunable functionality for efficient, brain-inspired information processing.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Neuromorphic computing mimics mammalian intelligence for efficient information processing.
- Artificial neurons require materials with specific nonlinear dynamics, posing design challenges.
- Controlling abrupt electronic transitions is crucial for device stability and preventing failure.
Purpose of the Study:
- To establish structure-function correlations between atomistic composition and nonlinear dynamics in artificial neurons.
- To investigate how modifying crystal structure impacts neuron-like functionality.
- To bridge the gap between statistical mechanics and quantum chemistry in phase transition descriptions.
Main Methods:
- Fabrication of oscillator circuits using millimeter-scale single crystals.
- High-resolution atomic structure determination via X-ray diffraction.
- Analysis of electronic transitions using temperature-variant X-ray spectroscopy.
Main Results:
- Selective Pb cation positioning in CuxV2O5 frameworks was shown to modify charge ordering.
- Atom-precise control was demonstrated to dampen abrupt first-order transitions.
- Suppression of Cu-ion shuttling by Pb ions was identified as the mechanism for transition broadening.
Conclusions:
- Mechanistic understanding of phase transitions enables site-selective modification strategies.
- Tuning the sharpness of first-order transitions is achievable through precise atomic manipulation.
- This approach provides a pathway for developing advanced neuromorphic computing devices with enhanced neuronal signal processing.
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