Wave Intuiting QNNs for Atomic Manipulation in the Genesis Mission.

Abstract

Classical approaches to materials engineering treat atoms as point-like particles and therefore miss their intrinsic wave properties (de Broglie wavelength λ = h/p) of protons and electrons inside specific periodic-table elements. This paper presents a wave-intuition framework for quantum neural networks (QNNs) that trains the network to directly intuit these wave behaviors, allowing correct identification of original atoms and precise guidance of atomic manipulation techniques. The framework enables scanning tunneling microscopy (STM), atomic layer deposition (ALD), ion channeling/implantation, atomic substitution, and synthescope-based atomic writing (e.g., tin insertion into graphene vacancies). It explicitly links subatomic wave properties and electron valency to macro-scale electrical and thermal behavior, overcoming the limitations of classical point-particle approximations. Applications include creation of defect-free 2D materials, quantum devices, and advanced catalysts. As a proof-of-concept, the same wave-intuition training recognizes high-frequency phenomena (MeV-scale dark matter) invisible to classical detectors (Compagnin et al., 2022). The clean, modular architecture integrates seamlessly with NET4EXA BXIv3 hardware, providing the Genesis Mission with a practical, low-risk pathway for engineering novel materials without rewriting legacy code from scratch. This work serves as the stable chassis for the Genesis Mission’s new-materials research thread, preserving decades of domain knowledge while enabling real-time atomic-scale engineering.