Reaction-Driven Three-Dimensional Generative Computing: A Framework for Constructing 3D Objects Through Local Chemical Transformation, Deposition, and Structural Growth
Abstract
Existing three-dimensional (3D) generative methods commonly construct objects by generating geometric representations, denoising latent structures, predicting spatial primitives, or reconstructing scenes from observations. This paper proposes Reaction-Driven Three-Dimensional Generative Computing (RD3D-GC), a conceptual framework in which local chemical transformation rules directly drive the formation of three-dimensional structures. Rather than treating chemical reactions solely as physical processes to be simulated or as constraints applied to an already generated object, RD3D-GC treats reaction events as generative operations that create, modify, connect, transform, or remove spatial material.
The framework represents a three-dimensional domain as a spatially distributed chemical state containing reactants, products, intermediates, material phases, and local structural attributes. Reaction rules operate on these states and interact with diffusion, transport, nucleation, deposition, dissolution, aggregation, and boundary conditions. Their cumulative effects produce evolving three-dimensional material configurations. A reaction-driven generator may be implemented using voxel grids, particle systems, implicit fields, meshes, point clouds, or hybrid geometric representations. The resulting structures can be decoded into conventional 3D assets or maintained as evolving material configurations.
This paper formalizes the framework through a coupled reaction–transport–geometry model, defines a discrete generative algorithm, introduces reaction-aware objectives, and proposes an evaluation protocol for testing structural fidelity, geometric diversity, chemical consistency, controllability, and computational efficiency. Potential applications include synthetic material design, porous structures, mineral-like morphologies, biomimetic forms, and procedural 3D asset generation. RD3D-GC is presented as a conceptual research proposal rather than an experimentally validated model. Its potential contribution lies in organizing chemical state transitions and material formation into an explicit three-dimensional generative architecture, while recognizing the substantial prior literature on reaction–diffusion systems, chemical pattern formation, and physically based growth simulation.
Related
- Collective Biological Construction: A Multi-Unit Generative Framework for Three-Dimensional Shape Formation Through Local Deposition and Collective Structural Evolution
- Phase-Transition-Driven 3D Generative Computing: A State-Evolution Framework for Constructing Three-Dimensional Geometry through Nucleation, Crystallization, Solidification, and Phase Separation
- Self-Assembly-Driven 3D Generative Computing: A Framework for Constructing Three-Dimensional Objects Through Learned Local Interactions
- Layerwise Deposition-Field Generative Modeling: A Process-Conditioned Framework for Three-Dimensional Generation Through Thin-Film Deposition and Surface Growth
- Deposition-Based 3D Generative Modeling: A Framework for Three-Dimensional Object Generation Through Gas-Phase Particle Deposition, Local Adsorption, and Branching Growth
BibTeX
@misc{zhang2026reactiondriven,
title = {{Reaction-Driven Three-Dimensional Generative Computing: A Framework for Constructing 3D Objects Through Local Chemical Transformation, Deposition, and Structural Growth}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23635926},
url = {https://zenodo.org/records/23635926},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reaction-Driven Three-Dimensional Generative Computing: A Framework for Constructing 3D Objects Through Local Chemical Transformation, Deposition, and Structural Growth
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23635926
UR - https://zenodo.org/records/23635926
AB - Existing three-dimensional (3D) generative methods commonly construct objects by generating geometric representations, denoising latent structures, predicting spatial primitives, or reconstructing scenes from observations. This paper proposes Reaction-Driven Three-Dimensional Generative Computing (RD3D-GC), a conceptual framework in which local chemical transformation rules directly drive the formation of three-dimensional structures. Rather than treating chemical reactions solely as physical processes to be simulated or as constraints applied to an already generated object, RD3D-GC treats reaction events as generative operations that create, modify, connect, transform, or remove spatial material. The framework represents a three-dimensional domain as a spatially distributed chemical state containing reactants, products, intermediates, material phases, and local structural attributes. Reaction rules operate on these states and interact with diffusion, transport, nucleation, deposition, dissolution, aggregation, and boundary conditions. Their cumulative effects produce evolving three-dimensional material configurations. A reaction-driven generator may be implemented using voxel grids, particle systems, implicit fields, meshes, point clouds, or hybrid geometric representations. The resulting structures can be decoded into conventional 3D assets or maintained as evolving material configurations. This paper formalizes the framework through a coupled reaction–transport–geometry model, defines a discrete generative algorithm, introduces reaction-aware objectives, and proposes an evaluation protocol for testing structural fidelity, geometric diversity, chemical consistency, controllability, and computational efficiency. Potential applications include synthetic material design, porous structures, mineral-like morphologies, biomimetic forms, and procedural 3D asset generation. RD3D-GC is presented as a conceptual research proposal rather than an experimentally validated model. Its potential contribution lies in organizing chemical state transitions and material formation into an explicit three-dimensional generative architecture, while recognizing the substantial prior literature on reaction–diffusion systems, chemical pattern formation, and physically based growth simulation.
ER -