将生成与预测结合，用隐空间设计实现分子结构高效设计。
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
- 用变分自编码器学隐空间，再用深度核学习优化它。
- 在QM9数据集上对生成结构的焓值预测精度高。
- 适合需要同时生成新结构和精准预测性质的研究者。
我们提出一种深度核学习变分自编码器（VAE-DKL）框架，将变分自编码器（VAE）的生成能力与深度核学习（DKL）的预测特性相结合。VAE学习高维数据的隐表示，实现新结构生成；而DKL通过高斯过程（GP）回归，将隐空间结构化以匹配目标属性。该方法在保留VAE生成能力的同时，提升了隐空间对基于GP的性质预测的支持。我们在两个数据集上进行了评估：一个具有预定义变分因子的结构化卡片数据集，以及以焓值为优化目标的QM9分子数据集。模型展现出高精度的性质预测能力，并能生成具有理想特征的训练外新结构。VAE-DKL框架为高通量材料发现与分子设计提供了兼具结构化隐空间组织与生成灵活性的可行路径。
原文摘要 · Abstract (English)
We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature of Deep Kernel Learning (DKL). The VAE learns a latent representation of high-dimensional data, enabling the generation of novel structures, while DKL refines this latent space by structuring it in alignment with target properties through Gaussian Process (GP) regression. This approach preserves the generative capabilities of the VAE while enhancing its latent space for GP-based property prediction. We evaluate the framework on two datasets: a structured card dataset with predefined variational factors and the QM9 molecular dataset, where enthalpy serves as the target function for optimization. The model demonstrates high-precision property prediction and enables the generation of novel out-of-training subset structures with desired characteristics. The VAE-DKL framework offers a promising approach for high-throughput material discovery and molecular design, balancing structured latent space organization with generative flexibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。