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.

使用机器学习方法模拟嗅觉体验，通过实验证实香水感知的量化。研究通过建立香水分子结构与人类嗅觉感知的混合模型来实现，该模型包含利用图和生成性神经网络的人工智能分子生成器、气味强度的量化和预测，以及为所需香气精心调配最佳溶剂和分子组合。此外，基于热力学的模型建立了嗅觉感知和液相浓度之间的联系。该方法使用迁移学习，并根据蒸气压力和香气注释选择最合适的分子。最终，通过数学优化问题来最小化新的嗅觉体验与目标体验之间的差异。通过使用现有的实验数据复制两种不同的嗅觉体验来验证了该方法。