Authors: Alexander Lukin
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-25)
Field: Materials Science — Materials Chemistry

Activating Novel Neural Pathways in the Data-Driven Materials Genome Through Phononic Information Processing: From Collective Atomic Vibrations to AI-Driven Inverse Design Activating Novel Neural Pathways in the Data-Driven Materials Genome Through Phononic Information Processing: From Collective Atomic Vibrations to AI-Driven Inverse Design PARADIGM-SHIFTING FRAMEWORK: This work presents the first comprehensive theoretical framework establishing materials as active quantum computers processing information through phononic dynamics, enabling genuine inverse design from target properties through vibrational architectures to structural implementations. This preprint establishes intellectual priority on a paradigm-shifting theoretical framework that fundamentally reconceptualizes materials science by recognizing materials not as passive objects with fixed properties, but as active quantum computers that continuously process information about themselves through phononic dynamics. The framework demonstrates that phonons—quantized collective atomic vibrations—uniquely occupy the interface between material structure (matter) and functional capabilities (information) through bidirectional causality: structure determines phonon modes via the dynamical matrix, while phonon populations determine thermodynamic stability through vibrational free energy. This circular causality establishes phonons as the fundamental computational substrate mediating between matter and information. We establish precise mathematical correspondence between neural network operations and phonon physics: matrix multiplications represent phonon scattering, nonlinear activations correspond to anharmonic interactions, and backpropagation mirrors thermodynamic evolution. Quantum neural networks prove essential because phonon superposition and entanglement enable parallel exploration of exponentially many configurations, fundamentally enabling discovery of non-intuitive designs inaccessible to classical approaches. The work presents five systematic mechanisms for activating novel neural pathways in phononic genomes: heteroatom doping creates localized modes coupling to host phonons; multilayer interfaces generate standing waves forming resonant circuits; electromagnetic fields enable dynamic switching; isotopic engineering programs information propagation rates; and remarkably, astrophysical synthesis environments reveal stabilization strategies conventional materials science would never discover. The framework extends universally across materials classes from two-dimensional materials to energetic systems, explaining diverse phenomena through unifying phononic information processing principles. Integration with autonomous experimentation platforms enables genuine inverse design where target properties propagate backward through phononic processing layers to determine required vibrational architectures, then identify structural parameters realizing those modes. Most profoundly, we establish information processing capability as a fundamental thermodynamic consideration joining energy and entropy. By making explicit the computational substrate all materials inherently possess, we enable rational design of materials as programmable information processors that determine their behaviors through quantum computation rather than passively possessing properties assigned by static structure alone. This preprint is submitted to Zenodo to establish intellectual priority on all concepts, mechanisms, and methodologies described herein while making them immediately available to the scientific community for collaborative development, critical evaluation, and creative extension. Document Type: Theoretical Framework / Conceptual Preprint Status: Establishes intellectual priority; detailed mathematical formalism and comprehensive experimental validation to follow in subsequent publications Keywords: phononic information processing, matter-information duality, quantum neural networks, inverse materials design, autonomous materials discovery, programmable matter, vibrational information channels, neural pathway activation, carbyne-enriched nanocomposites, phonon-mediated computation Phonon ; Superposition principle ; Quantum ; Artificial neural network ; Anharmonicity ; Coupling (piping) ; Quantum information ; Inverse Machine Learning in Materials Science ; Quantum many-body systems ; Mechanical and Optical Resonators