This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data.

翻译：本研究提出了一种以机器学习为核心的嗅觉体验复现方法，并通过香料感知的实验量化进行了验证。主要贡献包括一个连接香料分子结构与人类嗅觉感知的混合模型。该模型包含基于人工智能的分子生成器（采用图神经网络与生成式神经网络）、气味强度的量化与预测，以及针对目标香气优化溶剂与分子组合的精炼模块。此外，基于热力学模型建立了嗅觉感知与液相浓度之间的关联。该方法采用迁移学习，并根据蒸气压与香调选择最合适的分子。最终，通过构建数学优化问题，最小化新嗅觉体验与目标嗅觉体验之间的差异。利用现有实验数据对两种不同的嗅觉体验进行复现，验证了该方法。